Motion Data Extraction and Vectorization for Surveillance Bandwidth
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Solution Overview
Problem
Current motion sensing technologies face challenges such as lack of granularity in detection, privacy infringement, high data storage and bandwidth requirements, and power consumption issues, particularly in surveillance applications using PIR sensors and digital video cameras.
Innovation Solution
The motion data extraction and vectorization system (MDEVS) extracts and represents motion data as metadata, using an image sensor, motion data processor, and storage unit to capture and process video frames, generating matrices of vectors that define object motion without recording or transferring large video data, thus minimizing power, storage, and bandwidth usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital video camera is used for motion detection, then detection precision is improved, but data storage size increases
Solution Approach 1:
The patent extracts only the essential motion detection information from video data, separating the useful detection signal from the redundant video content. This allows achieving motion detection precision without storing or transmitting the full video data, thus resolving the contradiction between detection precision and data size.
Solution Approach 2:
The patent segments the video processing pipeline into distinct functional components: motion detection module that analyzes only motion-related features, and video recording module that captures full video only when needed. This segmentation allows the system to achieve motion detection precision while minimizing data storage by processing and storing only essential motion information separately from full video data.
2Measurement precision
If digital video camera records continuous video, then motion detection capability is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic sampling of video frames for motion detection instead of continuous processing. The system processes video frames at intervals, detecting motion events and triggering full video recording only when motion is detected. This periodic action maintains motion detection capability while significantly reducing power consumption by keeping the image sensor and processing units in low-power states between sampling intervals.
Solution Approach 2:
The patent extracts motion detection functionality as a separate low-power module that operates independently from the full video recording system. This extracted motion detection module consumes minimal power and can trigger the higher-power video recording subsystem only when necessary, resolving the contradiction between maintaining detection capability and reducing overall power consumption.
3Use of energy by moving object
If PIR sensor is used for motion detection, then power consumption is reduced, but detection granularity deteriorates
Solution Approach 1:
The patent merges the advantages of both PIR sensors and digital video cameras by combining a low-power PIR motion detection sensor with an image sensor in a hybrid system. The PIR sensor provides coarse motion detection with minimal power consumption, while the image sensor provides fine-grained visual details when triggered. This combination achieves both low power consumption and high detection granularity simultaneously.
Solution Approach 2:
The patent uses the PIR sensor as an intermediary trigger mechanism that activates the higher-resolution image sensor only when motion is detected. This intermediary approach allows the system to maintain low power consumption during normal operation while providing detailed granular detection capabilities when needed, resolving the contradiction between power efficiency and detection granularity.
4Quantity of substance
If compressed video data is stored, then data transmission bandwidth is reduced, but processing time increases
Solution Approach 1:
The patent extracts and stores only essential motion detection metadata (such as motion events, timestamps, and key frame references) separately from the full compressed video data. This extraction allows the system to transmit minimal data for motion analysis while maintaining the option to retrieve full video only when necessary, thus reducing both bandwidth usage and processing time simultaneously.
Data Source
AI summary
A method and a motion data extraction and vectorization system (MDEVS) extract and vectorize motion data of an object in motion with optimized data storage and data transmission bandwidth. The MDEVS includes an image sensor, a motion data processor, and a storage unit. The image sensor captures video data including a series of image frames of the object in motion. The motion data processor detects an object in motion from consecutive image frames, extracts motion data of the detected object in motion from each image frame, and generates a matrix of vectors defining the object in motion for each image frame using the extracted motion data. The motion data includes, for example, image data of the object, trajectory data, relative physical dimensions, a type of the object, time stamp of each image frame, etc. The storage unit maintains the generated matrix of vectors for local storage, transmission, and analysis.