Tracker Component for Behavioral Recognition Systems
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Solution Overview
Problem
Current video surveillance systems are labor-intensive and costly to maintain or adapt for different applications, as they require predefined definitions for object and behavior recognition, limiting their ability to recognize new objects or behaviors and associate related patterns of observed behavior.
Innovation Solution
A tracker component for a behavioral recognition system that receives video frames, updates object models based on their movement, and passes these models to a machine learning engine to identify patterns of behavior, using techniques like hierarchical block search and particle filter to efficiently track and recognize objects in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined definitions are used for object and behavior recognition, then the system can recognize objects and behaviors, but the system becomes labor-intensive and costly to maintain or adapt for different applications
Solution Approach 1:
The system performs self-learning by automatically acquiring knowledge about objects and behaviors through video analysis without requiring manual predefined definitions. The machine learning engine continuously updates object models and behavior patterns based on observed data, enabling the system to adapt to different applications autonomously
Solution Approach 2:
The system changes its recognition parameters dynamically by learning new object characteristics and behavior patterns from video data. Instead of using fixed predefined definitions, the system adjusts its recognition criteria based on observed instances, allowing adaptation to different applications through parameter evolution
2Reliability
If predefined patterns are used for behavior recognition, then the system can identify specific behaviors, but the system cannot learn or identify new patterns of behavior
Solution Approach 1:
The machine learning engine automatically learns new behavior patterns by analyzing video sequences and identifying recurring action sequences. The system improves its behavior recognition capability autonomously by extracting patterns from observed data without requiring manual programming of new behaviors
Solution Approach 2:
The system performs preliminary learning by continuously analyzing video data to build and update behavior patterns in advance. The machine learning engine prepares the system for recognizing new behaviors by pre-processing video sequences and extracting potential pattern information before formal recognition is needed
3Measurement precision
If frame-by-frame analysis is used to track objects, then the system can identify object movement, but the system requires real-time processing at high frame rates which increases computational complexity
Solution Approach 1:
The system performs preliminary tracking using a coarse object model in earlier frames to predict object location and characteristics. This preliminary action reduces the search space in subsequent frames, allowing for more precise tracking with reduced computational complexity by limiting the analysis to relevant regions
Solution Approach 2:
The tracking process is segmented into multiple stages: initial detection using coarse models, refinement using foreground patches, and final precision tracking. This segmentation allows the system to achieve high measurement precision while managing computational complexity by applying different levels of analysis to different phases of tracking
Data Source
AI summary
A tracker component for a computer vision engine of a machine-learning based behavior-recognition system is disclosed. The behavior-recognition system may be configured to learn, identify, and recognize patterns of behavior by observing a video stream (i.e., a sequence of individual video frames). The tracker component may be configured to track objects depicted in the sequence of video frames and to generate, search, match, and update computational models of such objects.