Vehicle Speed Estimation Using Video Motion Vectors
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Solution Overview
Problem
Conventional video-based vehicle speed estimation systems face significant computational burdens and challenges in achieving real-time processing due to high spatial and temporal resolutions required for accurate speed measurement and automatic license plate recognition, especially when dealing with compressed video streams.
Innovation Solution
A method and system that utilize video compression motion vector information to estimate vehicle speed by extracting and analyzing motion vectors, tracking vehicle features across frames, and embedding speed information into the compressed video stream for efficient processing and real-time screening.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high spatial and temporal resolutions are used for accurate speed estimation and ALPR, then measurement precision is improved, but device complexity and processing burden increase significantly
Solution Approach 1:
The patent extracts and utilizes motion vectors from compressed video streams, which are already computed during video compression. By taking out this pre-computed motion information and repurposing it for speed estimation, the system achieves accurate speed measurement without performing full video decomposition or complex image processing, thereby reducing processing complexity while maintaining precision
Solution Approach 2:
The patent uses motion vectors as an intermediary element between the compressed video stream and the speed estimation goal. Instead of directly processing high-resolution video frames for speed calculation, the system leverages motion vectors as a intermediate representation that contains the necessary motion information in a compressed form, reducing the computational burden while preserving measurement accuracy
2Measurement precision
If conventional video processing approaches are used for speed estimation, then speed measurement is achieved, but real-time processing capability deteriorates due to computational burden
Solution Approach 1:
The patent benefits from preliminary action because motion vectors are already computed during the video compression process before the speed estimation takes place. This pre-computed motion information is then reused for speed estimation, eliminating the need to perform separate, computationally intensive motion analysis on the original video frames, thereby enabling real-time processing
Solution Approach 2:
The patent changes the parameter representation from full video frames to motion vectors. By transforming the problem from processing high-dimensional pixel data to processing low-dimensional motion vector data, the system achieves the same speed estimation goal with significantly reduced computational complexity, enabling real-time processing capability
3Quantity of substance
If video compression is applied to reduce data rate, then bandwidth requirements are reduced, but access to motion information for speed estimation becomes more difficult
Solution Approach 1:
The patent converts the potential harm of video compression (loss of original pixel data) into a benefit by recognizing that compression algorithms inherently compute motion vectors as part of their operation. These motion vectors, which are side products of compression, are then exploited for speed estimation, turning the compression process from an obstacle into a useful resource
Data Source
AI summary
Automated low-complexity video-based vehicle speed estimation is described, that operates within the video stream to screen video sequences to identify and eliminate clear non-violators and/or identify and select potential violators within a multi-layer speed enforcement system, in which deeper layers provide enhanced accuracy on selected candidate (speeding) vehicles. Video motion vector clusters corresponding to a vehicle are identified and tracked across multiple frames of captured video. Movement of the motion vector clusters translated from pixels per second to real speed (e.g. miles per hour) to determine whether the vehicle was speeding. Estimated speed data is added to the video stream data is metadata, and video segments of candidate speeding vehicles are stored and/or transmitted for subsequent review (e.g. automated or manual).