Optical Flow Pattern Recognition for Surveillance Scene Analysis
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Solution Overview
Problem
Existing video surveillance systems face challenges in accurately identifying and classifying movement patterns, especially in crowded scenes where object segmentation becomes unreliable, leading to inaccuracies and increased computing time.
Innovation Solution
The system directly analyzes the optical flow field by comparing it with specified patterns, eliminating the need for object segmentation and tracking, and can identify movement patterns in real-time using minimal computing power, even in overpopulated scenes by examining the motion of small image regions or single pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object segmentation is used to identify moving objects by comparing current images with scene reference images, then moving objects can be tracked over time, but the system becomes unreliable in crowded scenes and requires significant computing time
Solution Approach 1:
The patent extracts only the essential motion information (optical flow vectors) from the image sequence, discarding the complex and computationally intensive object segmentation process. By focusing solely on pixel displacement patterns rather than full object identification, the system achieves both speed and reliability in crowded scenes.
Solution Approach 2:
Instead of segmenting objects first and then tracking their motion, the patent inverts the approach by directly analyzing optical flow patterns to identify movement patterns. This reversal eliminates the bottleneck of object segmentation and enables real-time analysis of crowd dynamics.
2Measurement precision
If object segmentation is performed to separate moving objects from background, then individual object tracking is possible, but the system fails in overpopulated scenes where objects overlap and become indistinguishable
Solution Approach 1:
The patent merges all individual object motions into a unified optical flow field representation. Instead of attempting to separate and track individual objects (which fails in crowds), the system combines all pixel motions and analyzes collective movement patterns, making it equally effective whether one person or one hundred people are present.
Solution Approach 2:
The patent uses excessive action by calculating optical flow for every pixel in the image rather than only for detected objects. This comprehensive approach ensures that no motion information is lost, enabling reliable detection of movement patterns even when objects are completely overlapping and individual boundaries are invisible.
Data Source
AI summary
The invention relates to video surveillance systems that are used, for example, for surveying public places, stations, streets, industrial estates, buildings or similar. Said video surveillance systems comprise one or more surveillance cameras that are oriented towards surveillance scenes and transfer image data streams, in the form of image sequences, to an evaluation center. The invention also relates to a device (1) for identifying and/or classifying a movement pattern in an image sequence of a surveillance scene comprising a plurality of moving objects, an interface (2) for recording the image sequence, a calculation module (5) for determining an optical flow field (10) in the surveillance scene by evaluating the image sequence and an identification module (6) that is designed in terms of programming and/or circuitry such that the optical field and/or partial areas thereof are compared to one or more patterns in order to identify the movement pattern in the image sequence.