Patent 9860450

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

Active provider: Google · gemini-2.5-flash

Derivative works

Defensive disclosure: derivative variations of each claim designed to render future incremental improvements obvious or non-novel.

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Defensive Disclosure for US Patent 9860450: Method and apparatus to correct digital video to counteract effect of camera shake

This document provides derivative variations of the core claims of US patent 9860450, aimed at establishing prior art for future incremental improvements in the field of digital video stabilization. The technical descriptions are intended to be sufficiently enabling for a person having ordinary skill in the art (PHOSITA) to reproduce or implement these variations.

Core Claims Addressed:

  • Claim 1 (Method): A method for use in an imaging device comprising an image sensor, a processor, a memory, and one or more motion sensors, the method comprising: capturing a sequence of images (video), detecting motion information, determining vertical/horizontal shift values, modifying images based on shifts, combining to obtain a final video, and storing the final video.
  • Claim 14 (Imaging Device): An imaging device comprising an image sensor, one or more motion sensors, a processor configured to perform the steps of Claim 1, and a memory configured to store the final video.
  • Claim 28 (Method with Compression): Similar to Claim 1, but explicitly includes applying a video compression technique to obtain a final video.
  • Claim 29 (Imaging Device with Compression): Similar to Claim 14, but explicitly includes a processor configured to apply a video compression technique to obtain a final video.

Derivative Variations

For Claim 1: Method for Video Stabilization with Motion Sensors

1. Material & Component Substitution: Utilizing Piezoelectric Motion Sensors and Ferroelectric Memory

  • Enabling Description: The method of Claim 1 is implemented using miniature piezoelectric accelerometers and gyroscopes for detecting motion information. These sensors, based on the piezoelectric effect, generate electrical charge in response to mechanical stress, offering high sensitivity and low power consumption suitable for compact imaging devices. The captured sequence of images and detected motion information are stored in ferroelectric random-access memory (FeRAM), which provides non-volatile data storage with high write endurance and fast read/write speeds, enabling rapid buffering and processing of video frames and motion data. The processor's shift value determination and image modification operations are optimized for the specific data access patterns of FeRAM.
flowchart TD
    A[Capture Image Sequence (Image Sensor)] --> B{Detect Motion (Piezoelectric Sensors)};
    B --> C[Store Raw Data (FeRAM)];
    C --> D[Processor: Determine Shifts];
    D --> E[Processor: Modify Images (FeRAM access)];
    E --> F[Combine Modified Images];
    F --> G[Store Final Video (FeRAM)];

2. Operational Parameter Expansion: Ultra-High-Resolution, High-Frame-Rate Video Stabilization for Scientific Imaging

  • Enabling Description: The method is adapted for stabilizing video streams captured at resolutions up to 8K UHD (7680x4320 pixels) and frame rates exceeding 240 frames per second (fps). Motion sensors, such as high-frequency micro-electro-mechanical systems (MEMS) gyroscopes and accelerometers, operate at sampling rates of 10 kHz or higher to accurately capture rapid, subtle movements. The processor, consisting of a parallel processing architecture (e.g., GPU clusters or multi-core DSPs), utilizes a pipelined algorithm for real-time determination of vertical and horizontal shift values, ensuring minimal latency. Image modification involves sub-pixel interpolation (e.g., bicubic or Lanczos resampling) to maintain image fidelity at extreme magnifications common in scientific applications, and the combining step aggregates these high-resolution, stabilized frames into an 8K/240fps final video.
graph TD
    A[Capture 8K@240fps Video] --> B{High-Freq MEMS Sensors};
    B --> C[Raw Motion & Image Data Stream];
    C --> D[Pipelined Parallel Processor];
    D -- Sub-pixel Interpolation --> E[Modified 8K Frames];
    E --> F[Combine & Output Final 8K@240fps Video];

3. Cross-Domain Application: Surgical Endoscopy Stabilization

  • Enabling Description: The method is applied to stabilize video feeds from surgical endoscopes during minimally invasive procedures. The imaging device is a flexible endoscope with a miniature image sensor and embedded motion sensors (e.g., fiber optic gyroscopes or strain gauges near the distal tip) that detect minute movements caused by surgeon tremor or patient respiration. The motion information is used by a dedicated processor unit to determine precise vertical and horizontal shifts for each video frame. Image modification aligns the live endoscopic view, correcting for shake and enabling a steadier visual field for the surgeon. The final stabilized video can be displayed on a surgical monitor or recorded for post-operative analysis and training.
sequenceDiagram
    participant E as Endoscope (Sensor + Imager)
    participant P as Processor Unit
    participant M as Surgical Monitor

    E ->> P: Transmit Live Video Frames (Sequence)
    E ->> P: Transmit Motion Data (Synchronous)
    P ->> P: Determine Shift Values (V/H)
    P ->> P: Modify Video Frames (Align)
    P ->> P: Combine/Render Stabilized Video
    P ->> M: Display Stabilized Video

4. Integration with Emerging Tech: AI-Driven Predictive Stabilization with IoT Sensor Network

  • Enabling Description: The method incorporates an AI-driven predictive model (e.g., a Recurrent Neural Network or Transformer-based architecture) for determining vertical and horizontal shift values. Instead of solely reacting to current motion, the AI model processes historical motion sensor data, real-time data from an array of IoT-enabled motion sensors (e.g., strategically placed accelerometers on the camera body, lens, and even the user's hand/gimbal), and contextual information (e.g., scene content analysis, ambient vibration patterns). This allows for proactive estimation of future camera movements and pre-computation of shifts, significantly reducing stabilization latency and artifacts. The IoT sensor network transmits data via low-power wireless protocols (e.g., Bluetooth Low Energy or Zigbee) to a central processing unit for aggregate motion analysis.
graph TD
    subgraph IoT Sensor Network
        S1[Hand Sensor] -- BLE/Zigbee --> R(Data Aggregator/Router)
        S2[Camera Body Sensor] -- BLE/Zigbee --> R
        S3[Lens Sensor] -- BLE/Zigbee --> R
    end
    R --> D[Raw Motion Data Stream];
    D -- Historical Data --> AI[AI Predictive Model (RNN/Transformer)];
    AI -- Predicted Shifts --> P(Processor Unit);
    P -- Real-time Video Frames --> P;
    P -- Proactive Correction --> F[Final Stabilized Video];

5. The "Inverse" or Failure Mode: Safe-Mode Low-Power Stabilization

  • Enabling Description: The method includes a safe-mode operation for low-power or limited-functionality scenarios. When battery levels fall below a threshold or system resources are constrained, the imaging device automatically switches to a low-power stabilization mode. In this mode, motion detection is simplified, perhaps by reducing the sampling rate of motion sensors or activating only a subset of sensors. Shift value determination uses a coarser approximation algorithm (e.g., block-matching motion estimation instead of full pixel-level shifts), prioritizing computational efficiency over maximal image quality. The image modification might involve simpler averaging of frames or only correcting dominant translational motion, while rotational or perspective distortions are ignored. The final video is stored at a reduced resolution or bitrate, ensuring continued recording and basic stabilization until resources are restored or capture terminates.
stateDiagram-v2
    state Normal_Operation {
        [*] --> Capture_High_Res : Start
        Capture_High_Res --> Process_Full_Stabilization : Motion Detected
        Process_Full_Stabilization --> Store_High_Quality_Video : Done
    }
    state Low_Power_Mode {
        [*] --> Capture_Low_Res : Start
        Capture_Low_Res --> Process_Basic_Stabilization : Motion Detected
        Process_Basic_Stabilization --> Store_Reduced_Quality_Video : Done
    }
    Normal_Operation --> Low_Power_Mode : Low Battery / Resource Constraint
    Low_Power_Mode --> Normal_Operation : Power Restored / Resources Available

For Claim 14: Imaging Device for Video Stabilization with Motion Sensors

1. Material & Component Substitution: Modular Carbon Fiber Body with MEMS IMUs and Custom ASIC Processor

  • Enabling Description: The imaging device comprises a modular housing constructed from carbon fiber composites, offering superior strength-to-weight ratio and vibration dampening compared to traditional metal or plastic bodies. The one or more motion sensors are integrated as a redundant array of miniaturized MEMS Inertial Measurement Units (IMUs), strategically placed within the camera body and lens mount for comprehensive 6-degrees-of-freedom (6-DoF) motion detection. The processor is a custom-designed Application-Specific Integrated Circuit (ASIC) optimized for real-time execution of the shift determination and image modification algorithms. This ASIC includes dedicated hardware accelerators for image resampling and blending, ensuring ultra-low power consumption and high processing throughput. The memory consists of stacked 3D NAND flash for high-density video storage, coupled with high-bandwidth GDDR6 for frame buffers.
classDiagram
    class ImagingDevice {
        +CarbonFiberHousing
        +ImageSensor[]
        +MEMSIMU[] MotionSensors
        +CustomASICProcessor
        +3DNANDMemory
        +GDDR6FrameBuffer
    }
    ImagingDevice --> ImageSensor
    ImagingDevice --> MEMSIMU
    ImagingDevice --> CustomASICProcessor
    ImagingDevice --> 3DNANDMemory
    ImagingDevice --> GDDR6FrameBuffer

2. Operational Parameter Expansion: Submersible, Pressure-Tolerant Imaging Device for Deep-Sea Exploration

  • Enabling Description: The imaging device is designed for deep-sea exploration, encased in a titanium alloy pressure housing capable of withstanding pressures up to 100 MPa (corresponding to ~10,000 meters depth). The image sensor is a back-illuminated scientific CMOS (sCMOS) array optimized for low-light conditions prevalent in deep-sea environments. Motion sensors are hardened piezoelectric transducers and fiber-optic gyroscopes, hermetically sealed to prevent water ingress and maintain functionality under extreme pressure and low temperatures. The processor, also housed in the pressure vessel, employs a fault-tolerant, radiation-hardened architecture to ensure reliable operation. Shift determination and image modification algorithms are pre-calibrated for potential optical distortions unique to underwater imaging, such as chromatic aberration and refraction effects from viewing ports.
graph LR
    A[TitaniumPressureHousing] --> B[Back-Illuminated_sCMOS_ImageSensor];
    A --> C[Hardened_Piezoelectric_MotionSensors];
    A --> D[FiberOpticGyroscopes];
    A --> E[Fault-Tolerant_Radiation-Hardened_Processor];
    E -- Controls --> B;
    E -- Processes --> C;
    E -- Processes --> D;
    E -- Stores --> F[Memory_for_Final_Video];
    F -- Encased_within --> A;

3. Cross-Domain Application: Drone-Mounted Aerial Survey System

  • Enabling Description: The imaging device is specifically configured for integration with unmanned aerial vehicles (UAVs) for aerial survey and mapping. It features a lightweight, gimbal-stabilized housing with an integrated high-resolution global shutter image sensor to minimize rolling shutter artifacts. The motion sensors comprise a high-precision IMU (including gyroscopes, accelerometers, and magnetometers) rigidly coupled to the image sensor, capable of detecting subtle drone vibrations and platform movements. The processor is a compact, low-power system-on-chip (SoC) with dedicated hardware acceleration for real-time video stabilization and geospatial metadata embedding. The device determines precise shift values to correct for residual gimbal imperfections and wind-induced drone movements, ensuring georeferencing accuracy of the captured video for photogrammetry applications.
flowchart TD
    A[UAV Platform] --> B(Gimbal-Stabilized Mount);
    B --> C[Drone-Mounted_ImagingDevice];
    C -- Contains --> D[Global_Shutter_ImageSensor];
    C -- Contains --> E[High-Precision_IMU_MotionSensors];
    C -- Contains --> F[Compact_SoC_Processor];
    F -- Determines Shifts --> D;
    F -- Modifies/Combines --> G[Memory_for_Final_Video];
    G -- Outputs --> H[Georeferenced_Stabilized_Video];

4. Integration with Emerging Tech: Edge AI-Enhanced Smart Surveillance Camera

  • Enabling Description: The imaging device is a smart surveillance camera featuring an embedded Edge AI processor (e.g., a neural processing unit or dedicated AI accelerator). The motion sensors are high-accuracy MEMS IMUs. The Edge AI is trained to distinguish between intentional camera movements (e.g., pan/tilt by operator) and unintentional shake, dynamically adjusting stabilization parameters. Furthermore, it can perform semantic segmentation of the video stream to identify regions of interest (e.g., human subjects, vehicles). The processor prioritizes stabilization within these regions, potentially allowing a slight blur in static background areas if it optimizes computational resources or improves target tracking. Blockchain technology is integrated for secure, verifiable logging of camera operational parameters, motion events, and stabilization applied, ensuring chain of custody and tamper-proofing for forensic video evidence.
graph TD
    A[ImageSensor] --> B[VideoStream];
    M[MEMS_IMUs] --> C[MotionData];
    B & C --> E[Edge_AI_Processor];
    E -- Identifies Intentional/Unintentional Motion --> F(Stabilization Module);
    E -- Semantic Segmentation --> F;
    F --> G[Modified_Images];
    G --> H[Combined_Final_Video];
    H --> I[Memory];
    I --> J[Blockchain_Logger];
    E -- Logs Parameters & Events --> J;

5. The "Inverse" or Failure Mode: Redundant, Fail-Safe Micro-Camera System

  • Enabling Description: The imaging device is a redundant micro-camera system for critical applications where video capture must continue despite component failure. It comprises an array of three identical, small-form-factor imaging modules, each with its own image sensor, motion sensors, and local processing unit. If one module detects an internal fault (e.g., motion sensor failure, processor error, or memory corruption), it signals a master controller. The master controller then dynamically reconfigures the system to use data from the remaining functional modules. In a single module failure, the system falls back to a 2-camera mode, potentially sacrificing some spatial resolution or stabilization robustness but maintaining core functionality. If two modules fail, the system operates in a minimal single-camera mode with only basic stabilization or raw capture, signaling a critical alert. This fail-safe design ensures graceful degradation rather than catastrophic failure.
stateDiagram-v2
    state Normal_Operation {
        [*] --> All_Modules_Active : System Start
        All_Modules_Active --> Full_Stabilization : No Faults
    }
    state Degraded_Mode_1 {
        [*] --> Module_1_Failed : Fault Detected
        Module_1_Failed --> Two_Modules_Active : Reconfigure
        Two_Modules_Active --> Reduced_Stabilization : Continue
    }
    state Degraded_Mode_2 {
        [*] --> Module_2_Failed : Fault Detected (from Degraded_Mode_1)
        Module_2_Failed --> One_Module_Active : Reconfigure
        One_Module_Active --> Basic_Stabilization_or_Raw : Continue
    }

    Normal_Operation --> Degraded_Mode_1 : Module 1 Failure
    Degraded_Mode_1 --> Degraded_Mode_2 : Module 2 Failure

For Claim 28: Method for Video Stabilization with Compression

1. Material & Component Substitution: Hybrid Hardware/Software Codec with Quantum-Dot Image Sensor

  • Enabling Description: The method of Claim 28 utilizes a hybrid hardware/software video compression technique. After modifying and combining the images, the resulting video stream is fed into a dedicated hardware video encoder (e.g., an H.265/HEVC ASIC) for high-speed, power-efficient compression. Concurrently, a software-based post-processing engine (running on a general-purpose processor) applies perceptual quality enhancements or adaptive bitrate optimization not typically handled by basic hardware codecs. The initial image capture is performed by an image sensor utilizing quantum-dot technology, which offers superior color reproduction and wider dynamic range compared to traditional CMOS sensors, providing higher quality input for both stabilization and subsequent compression.
flowchart TD
    A[Capture Image Sequence (Quantum-Dot Sensor)] --> B[Detect Motion];
    B --> C[Determine Shifts];
    C --> D[Modify Images];
    D --> E[Combine Modified Images];
    E --> F{Hybrid Video Compression};
    F --> F1[Hardware Encoder (H.265 ASIC)];
    F --> F2[Software Post-Processing (Adaptive Bitrate)];
    F1 & F2 --> G[Final Compressed Video];
    G --> H[Store Final Video];

2. Operational Parameter Expansion: Real-time, Ultra-Low Latency Video Compression for Remote-Controlled Systems

  • Enabling Description: The method is optimized for real-time video stabilization and compression with ultra-low latency, crucial for remote-controlled robotics or telepresence applications where feedback delay is critical. This involves capturing images at very high frame rates (e.g., 500+ fps), performing shift determination and image modification on a frame-by-frame basis with minimal buffering. The video compression technique used is a custom wavelet-based codec or a highly optimized low-latency H.264/H.265 profile, specifically designed to minimize end-to-end delay rather than solely focusing on file size. This may involve predictive coding across minimal frames or reducing group-of-pictures (GOP) sizes. Motion sensor data is timestamped with nanosecond precision and processed concurrently with image data to ensure synchronous shifts.
sequenceDiagram
    participant I as ImageSensor
    participant M as MotionSensors
    participant P as Processor (Low-Latency)
    participant C as Custom_Codec
    participant N as Network_Stream

    I ->> P: Frame_N (High FPS)
    M ->> P: MotionData_N (Synchronous)
    P ->> P: Determine/Modify(Frame_N, MotionData_N)
    P ->> C: Modified_Frame_N
    C ->> N: Compressed_Frame_N (Ultra-low latency)
    loop Continuous Operation
        I ->> P: Frame_N+1
        M ->> P: MotionData_N+1
        P ->> P: Determine/Modify(Frame_N+1, MotionData_N+1)
        P ->> C: Modified_Frame_N+1
        C ->> N: Compressed_Frame_N+1
    end

3. Cross-Domain Application: Live Sports Broadcasting Stabilization and Archival

  • Enabling Description: The method is deployed in live sports broadcasting to stabilize dynamic camera footage (e.g., from handheld cameras, steadicams, or wired cams) and prepare it for real-time transmission and archival. The imaging device detects rapid, complex camera movements characteristic of sports coverage. Shift values are determined to keep athletes or the field of play centered and stable. The video compression technique applied is a professional broadcast-grade codec (e.g., MPEG-2 Transport Stream for live, JPEG 2000 or ProRes for archival), allowing for high-quality, low-artifact output suitable for television networks, while simultaneously enabling flexible bitrates for different distribution channels (e.g., 4K HDR for main broadcast, 1080p for streaming).
graph TD
    A[Live_Sports_Camera] --> B{Detect Motion};
    B --> C[Capture Video Stream];
    C & B --> D[Processor: Determine/Modify Shifts];
    D --> E[Combined_Stabilized_Video];
    E --> F{Broadcast-Grade_Compression};
    F -- Real-time --> G[Live_Broadcast_Feed];
    F -- High-Quality --> H[Archival_Storage];

4. Integration with Emerging Tech: Decentralized Video Stabilization with Blockchain for Content Integrity

  • Enabling Description: The method extends to a decentralized network where multiple imaging devices (Claim 14 devices) collaboratively stabilize and compress video, with blockchain technology ensuring content integrity. Each imaging device captures video and motion data, performs initial shift determination, and applies a preliminary stabilization. Before final combination and compression, selected frames and associated metadata (motion vectors, stabilization parameters) are securely hashed and recorded on a blockchain. A network of distributed processors then performs the final image modification and combination, verifying the integrity of the input frames against the blockchain records. The final compressed video is also hashed and recorded, providing an immutable record of its origin and processing steps, valuable for journalistic integrity or legal evidence.
sequenceDiagram
    participant D1 as Device 1 (Imager+Sensors)
    participant D2 as Device 2 (Imager+Sensors)
    participant DP as Distributed Processors
    participant B as Blockchain Network

    D1 ->> DP: Send Raw Frames & Motion Data
    D2 ->> DP: Send Raw Frames & Motion Data
    DP ->> DP: Initial Shift & Stabilization (per device)
    DP ->> B: Hash Frames & Metadata (Record)
    B -->> DP: Verification Confirmation
    DP ->> DP: Final Combination & Compression
    DP ->> B: Hash Final Video (Record)
    B -->> DP: Final Video Integrity Confirmed
    DP ->> DP: Store Final Video

5. The "Inverse" or Failure Mode: Adaptive Compression for Bandwidth Constraint Degradation

  • Enabling Description: The method incorporates an adaptive video compression technique that gracefully degrades quality under severe bandwidth constraints, a common failure mode in wireless video transmission. If the network uplink bandwidth drops below a critical threshold, the processor dynamically adjusts the compression parameters. This begins with increasing the quantization parameter (QP), reducing chroma subsampling, and then progressively lowering resolution or frame rate as conditions worsen. The stabilization algorithm may also adapt, reducing the complexity of shift determination or motion compensation to save processing cycles that can be reallocated to compression, ensuring a continuous (though lower quality) video stream is maintained rather than dropping frames entirely. This ensures a "minimum viable video" is always transmitted.
graph TD
    A[Detect Network Bandwidth] --> B{Bandwidth Threshold Exceeded?};
    B -- Yes --> C[Adaptive Compression Module];
    C --> C1[Increase QP];
    C --> C2[Reduce Chroma Subsampling];
    C --> C3[Lower Resolution/Frame Rate];
    C --> C4[Simplify Stabilization Algo];
    C1 & C2 & C3 & C4 --> D[Apply Compression];
    D --> E[Transmit Degraded Video];
    B -- No --> F[Standard Compression];
    F --> G[Transmit High-Quality Video];
    E & G --> H[Store Final Video];

For Claim 29: Imaging Device for Video Stabilization with Compression

1. Material & Component Substitution: Gallium Nitride (GaN) Power Electronics and Neural Network Processor

  • Enabling Description: The imaging device integrates Gallium Nitride (GaN) power electronics for highly efficient power management, critical for compact, long-endurance devices requiring significant processing power for video stabilization and compression. The processor is a dedicated Neural Network Processor (NNP) or AI accelerator, specifically designed for executing deep learning models. This NNP handles both the determination of vertical and horizontal shift values (using a trained CNN for robust motion estimation) and aspects of the video compression pipeline (e.g., neural network-based image enhancement before encoding, or neural compression itself). The image sensor is a low-noise, high-sensitivity global shutter CMOS sensor. The memory includes both high-speed LPDDR5 for temporary frame storage and UFS 4.0 for high-throughput final video storage.
classDiagram
    class ImagingDevice {
        +GlobalShutterCMOS
        +MotionSensors
        +NNPProcessor
        +GaNPowerElectronics
        +LPDDR5Memory
        +UFS4_0Memory
    }
    ImagingDevice --> GlobalShutterCMOS
    ImagingDevice --> MotionSensors
    ImagingDevice --> NNPProcessor
    ImagingDevice --> GaNPowerElectronics
    ImagingDevice --> LPDDR5Memory
    ImagingDevice --> UFS4_0Memory

2. Operational Parameter Expansion: Cryogenic Imaging Device for Astronomical Observations

  • Enabling Description: The imaging device is designed for use in cryogenic environments, such as space telescopes or specialized laboratory setups, operating at temperatures as low as 4 Kelvin. The image sensor is a specialized cryo-CMOS sensor, and motion sensors are superconducting quantum interference devices (SQUIDs) or other ultra-low temperature compatible accelerometers/gyroscopes, capable of detecting minute vibrations. The processor and memory components are radiation-hardened and selected for stable operation at cryogenic temperatures, potentially utilizing superconductive interconnects for extreme speed. The video compression technique is optimized for scientific data integrity, possibly employing lossless or near-lossless compression algorithms to preserve subtle astronomical features, and is performed by a dedicated cryo-compatible processing unit before transmission or storage.
graph TD
    A[CryogenicEnvironment] --> B(Cryo_ImagingDevice);
    B -- Contains --> C[Cryo-CMOS_ImageSensor];
    B -- Contains --> D[SQUID_MotionSensors];
    B -- Contains --> E[Radiation-Hardened_Cryo-Processor];
    E -- Processes --> C;
    E -- Processes --> D;
    E -- Applies --> F[Lossless/Near-Lossless_Compression];
    F --> G[Cryo-Compatible_Memory_for_Video];

3. Cross-Domain Application: Automated Agricultural Monitoring Drone

  • Enabling Description: The imaging device is implemented as part of an autonomous agricultural monitoring drone system. The image sensor captures multispectral or hyperspectral video sequences of crops. Motion sensors (integrated IMU on the drone) detect precise movements from wind gusts or drone maneuvers. The processor determines shift values to stabilize the video, ensuring accurate spatial alignment of successive frames for vegetation index calculation. The video compression technique applied is optimized for efficient transmission of large datasets over wireless links in agricultural fields (e.g., using sparse coding or region-of-interest (ROI) compression to prioritize crop health indicators over background), sending compressed data to a ground station for analysis.
flowchart LR
    A[Autonomous_AgriDrone] --> B(Multispectral_ImageSensor);
    A --> C(Drone_IMU_MotionSensors);
    B & C --> D[Onboard_Processor_Unit];
    D --> E[Stabilization_Module];
    E --> F[ROI-Optimized_Video_Compression];
    F --> G[Wireless_Telemetry_Module];
    G --> H[Ground_Station_Analysis];

4. Integration with Emerging Tech: Real-time Digital Twin Generation with AI & VR/AR Output

  • Enabling Description: The imaging device is a component of a system for generating real-time digital twins of physical environments, leveraging AI-driven stabilization and immersive output. The image sensor captures high-fidelity video, while high-precision motion sensors provide spatial tracking data. An onboard AI processor performs stabilization and concurrently generates 3D point clouds or mesh representations from the stabilized video. The processor is configured to combine these modified images and spatial data, applying a specialized video compression technique (e.g., a volumetric video codec or a codec optimized for 3D reconstruction data). The output is streamed for real-time visualization in a Virtual Reality (VR) or Augmented Reality (AR) headset, allowing users to interact with a stabilized, live digital twin of the environment.
sequenceDiagram
    participant I as ImageSensor
    participant M as MotionSensors
    participant P as AI_Processor (Onboard)
    participant V as VR/AR_Headset

    I ->> P: High-Fidelity_Video_Frames
    M ->> P: High-Precision_Motion_Data
    P ->> P: Stabilize_Video_Frames (AI-driven)
    P ->> P: Generate_3D_Point_Cloud (from stabilized video)
    P ->> P: Volumetric_Video_Compression
    P ->> V: Stream_Real-time_Digital_Twin_Data

5. The "Inverse" or Failure Mode: Forensic Event Recorder with Write-Once Read-Many (WORM) Memory

  • Enabling Description: The imaging device acts as a forensic event recorder in high-risk environments (e.g., vehicle dashcam, industrial safety monitoring). Its primary "failure mode" consideration is data integrity against tampering or accidental deletion. The device uses a Write-Once Read-Many (WORM) memory for storing the final video, preventing any post-capture modification. If a major impact or system fault is detected by the motion sensors (e.g., an accelerometer reading exceeding a crash threshold), the processor immediately triggers a final, robust stabilization and compression cycle on the buffered video segment preceding and during the event. This specific event video is then quickly written to the WORM memory. The video compression technique employed is a fixed, high-quality, tamper-evident codec (e.g., a specific H.264 profile with embedded cryptographic hashes in metadata) to ensure the stored footage is admissible as evidence. Regular video capture continues with standard compression to a volatile buffer or a different storage medium.
stateDiagram-v2
    state Normal_Recording {
        [*] --> Buffer_Video_&_Motion : Start
        Buffer_Video_&_Motion --> Standard_Compression : Regular Ops
        Standard_Compression --> Volatile_Storage : Store
    }
    state Event_Recording {
        Event_Detected --> Trigger_Forensic_Capture : High G-Force / Fault
        Trigger_Forensic_Capture --> Final_Robust_Stabilization : Process Buffered Data
        Final_Robust_Stabilization --> Tamper_Evident_Compression : Apply Codec
        Tamper_Evident_Compression --> WORM_Memory_Storage : Store Permanently
    }
    Normal_Recording --> Event_Recording : Motion Sensor Alert

Combination Prior Art Scenarios

These scenarios describe how the concepts within US9860450 could be combined with existing open-source standards, thereby contributing to the prior art and rendering similar future developments obvious.

1. Integration with FFmpeg for General-Purpose Video Editing & Processing Workflows

  • Scenario Description: An implementation wherein the methods described in Claim 1 and Claim 28 of US9860450 are provided as a set of open-source filter plugins or libraries for the FFmpeg multimedia framework. Specifically, motion information detected by the device's sensors (e.g., an IMU log file) is ingested alongside the video stream. A custom FFmpeg filter would then parse this motion data, calculate the vertical and horizontal shift values for each frame (as described in Claim 1), and apply these shifts using FFmpeg's existing video manipulation capabilities (e.g., setpts, tps, crop, pad filters, or custom pixel-level transformations). The modified images are combined, and the resulting stabilized video is then subjected to FFmpeg's vast array of video compression techniques (e.g., libx264 for H.264 or libvpx for VP9, as per Claim 28), allowing for a highly flexible and widely adaptable video stabilization and compression pipeline. This publicly available FFmpeg plugin, documented with enabling descriptions, renders any similar "ingest motion data for stabilization then compress" approach obvious for general video processing.
graph TD
    A[Raw Video (e.g., MP4)] --> B(FFmpeg Input);
    M[Motion Sensor Log (e.g., CSV)] --> B;
    B --> C{Custom FFmpeg Filter Plugin};
    C -- Parse Motion Data --> D[Calculate V/H Shifts];
    D -- Apply Shifts --> E[FFmpeg Video Manipulation (e.g., setpts, crop)];
    E --> F[Combined Stabilized Video Stream];
    F --> G{FFmpeg Encoder (e.g., libx264)};
    G --> H[Final Compressed Video File];

2. Real-time Stabilization for Robotics Platforms using Robot Operating System (ROS) and OpenCV

  • Scenario Description: The principles of the imaging device (Claim 14) and its method (Claim 1) are implemented on a mobile robotics platform (e.g., a ground robot or drone) running the Robot Operating System (ROS). The imaging device's motion sensors publish their data (e.g., IMU messages) as standard ROS topics. The image sensor publishes raw video frames as another ROS topic. A ROS node, implemented using the OpenCV library for image processing, subscribes to both these topics. This node processes the incoming motion data to determine vertical and horizontal shift values and uses OpenCV functions (e.g., warpAffine or warpPerspective) to modify and combine the video frames in real-time, producing a stabilized video stream that is published as a new ROS topic. This stabilized stream can then be used by other ROS nodes for navigation, object detection, or transmitted for remote viewing. This open-source integration would make the combination of motion sensors and image processing for video stabilization on robotics platforms universally obvious.
graph LR
    subgraph Imaging Device
        I[Image Sensor] -- Publishes /camera/image_raw --> ROS_T(ROS Topic Bus)
        M[Motion Sensors (IMU)] -- Publishes /imu/data --> ROS_T
    end
    subgraph Stabilization Node (OpenCV)
        SN[ROS Node: Video Stabilizer] --> Sub_IR(Subscribes /camera/image_raw)
        SN --> Sub_IMU(Subscribes /imu/data)
        Sub_IMU -- Motion Data --> SN_P(Process Motion)
        Sub_IR -- Image Frame --> SN_P
        SN_P -- Calculate V/H Shifts --> SN_M(Modify Image w/ OpenCV)
        SN_M --> Pub_SV(Publishes /camera/image_stabilized)
    end
    ROS_T --> SN
    SN --> ROS_T

3. Blockchain-Verified Motion Data and Stabilization Metadata with WebM/VP9 Open Standard

  • Scenario Description: The method of Claim 28 and apparatus of Claim 29 are extended to publicly record motion information and stabilization metadata on a public blockchain, specifically for video encoded using the open-source WebM container format with VP9 video codec. The imaging device detects motion, determines shifts, modifies images, and combines them. Before applying WebM/VP9 compression, the processor calculates cryptographic hashes of the raw motion sensor data (or derived transfer functions) and the stabilization parameters (e.g., the applied vertical and horizontal shifts for keyframes). These hashes, along with timestamps, are embedded within the WebM metadata (e.g., using Matroska elements or custom WebM tracks) and simultaneously submitted as transactions to a public blockchain (e.g., Ethereum or a specialized media blockchain). This provides an immutable, auditable record of the stabilization process and source motion data, ensuring transparency and verifiable content integrity for publicly shared videos, while utilizing a widely adopted open video standard.
flowchart TD
    A[Capture Video & Motion Data] --> B[Determine/Apply Shifts];
    B --> C[Stabilized Video Frames];
    C --> D{Hash Motion Data & Stabilization Metadata};
    D --> E[Embed Hashes in WebM Metadata];
    C & E --> F[WebM/VP9 Encoder];
    F --> G[Final WebM Video File];
    D --> H[Submit Hashes to Blockchain];
    G --> I[Store Final Video];

Generated 6/1/2026, 10:47:27 PM