Coherent Motion Region Detection in Crowded Video Streams
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Solution Overview
Problem
Current automated systems face challenges in accurately and consistently detecting coherent motion patterns in video streams, falling short of human visual perception capabilities, particularly in crowded environments.
Innovation Solution
The algorithm identifies discrete moving objects by calculating trajectory similarity factors between feature point tracks in a three-dimensional space, including a time dimension, to generate coherent motion regions, which are robust to noise and long-term tracking, and uses a processor to execute machine-executable instructions for real-time video image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated systems use traditional feature point tracking methods to detect moving objects, then the system complexity is reduced, but the detection accuracy and sensitivity fall short of human visual perception capabilities
Solution Approach 1:
The patent extends traditional two-dimensional feature point tracking into three-dimensional space by incorporating temporal dimension. Feature point tracks are represented as sequences of (x, y, t) coordinates, where t is the time dimension. This dimensional extension allows the system to capture coherent motion patterns over time, significantly improving detection accuracy while maintaining computational feasibility through efficient 3D spatial-temporal algorithms.
Solution Approach 2:
The patent segments the video stream into discrete feature point tracks, where each track represents a potential moving object. By dividing the complex task of detecting multiple objects into individual track analysis, the system achieves high detection accuracy for each object while managing overall system complexity through modular processing of segmented tracks.
2Reliability
If the system tracks feature points over long time periods to improve object identification consistency, then detection reliability improves, but the system becomes more sensitive to noise and computational load increases
Solution Approach 1:
The patent merges multiple feature point tracks that exhibit coherent motion patterns into unified object representations. By combining information from multiple tracks that move together in the 3D space-time volume, the system achieves reliable and consistent object identification over long time periods while averaging out noise effects, thereby improving reliability without proportionally increasing noise sensitivity.
3Measurement precision
If the system analyzes every feature point track in detail to achieve high detection accuracy, then measurement precision improves, but processing speed and productivity decrease
Solution Approach 1:
The patent extracts and focuses analysis only on coherent motion regions where multiple feature point tracks exhibit consistent motion patterns. By taking out and isolating these relevant regions from the entire video stream, the system achieves high detection accuracy for moving objects while avoiding unnecessary processing of static or irrelevant areas, thereby maintaining processing speed and productivity.
Data Source
AI summary
Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.