Video Analytics Exclusion Zone for False Positive Suppression
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Solution Overview
Problem
Current video analytics systems face challenges in accurately distinguishing between true object tracks and false positives, particularly in regions defined as exclusion zones, leading to inefficient processing and potential misidentification of moving objects.
Innovation Solution
The system employs a method to identify and suppress blob trackers within exclusion zones by determining if the bounding region of a blob tracker intersects with a defined exclusion zone, preventing the output of trackers in such areas and allowing tracking to resume when the object moves outside the zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video analytics systems track all detected objects in the scene, then the system can detect all moving objects, but false positives increase due to objects in exclusion zones being tracked
Solution Approach 1:
The video scene is segmented into tracked zones and exclusion zones using spatial boundaries. Objects are tracked only in permitted regions while exclusion zones are explicitly defined where tracking is suppressed, thereby reducing false positives without compromising overall tracking accuracy.
Solution Approach 2:
Different tracking behaviors are applied to different spatial regions: full tracking is enabled in allowed zones while tracking is suppressed in exclusion zones. This local differentiation enables the system to maintain high tracking accuracy in valid regions while eliminating false positives from excluded regions.
2Object-generated harmful factors
If the system suppresses all trackers in exclusion zones, then false positives are reduced, but processing efficiency decreases due to continuous monitoring of exclusion zone boundaries
Solution Approach 1:
Exclusion zones are pre-defined and stored before video processing begins. The system performs a preliminary check to determine whether detected objects fall within exclusion zones, allowing for rapid suppression decisions without continuous complex boundary monitoring, thereby maintaining processing efficiency.
3Measurement precision
If the system monitors bounding regions continuously to detect exclusion zone intersections, then tracking accuracy is maintained, but computational complexity increases
Solution Approach 1:
The system replaces continuous geometric intersection monitoring with a simplified zone-based suppression mechanism. Once an object is detected to be within an exclusion zone, tracking is suppressed without requiring continuous intersection calculations, significantly reducing computational complexity while maintaining sufficient detection accuracy.
Data Source
AI summary
Provided are methods, apparatus, and computer-readable mediums for tracking objects that intersect with an exclusion zone defined for a scene being captured by a video camera. An exclusion zone can delineate an area of a video frame where background objects may be moving. The exclusion zone informs an object tracking system that objects within the exclusion zone should not be tracked. In various implementations, the object tracking system can determine that a bounding box for a blob intersects with the exclusion zone. The object tracking system can further, based on the bounding box intersecting with the exclusion zone, prevent outputting of a blob tracker associated with the blob.