Video Surveillance Object Tracking Using Correlation Filter Templates
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Solution Overview
Problem
Existing video surveillance systems face challenges in accurately tracking faces across different video sequences due to variations in face shape and motion, especially when faces are blocked or blurred.
Innovation Solution
A system and method for tracking objects in video surveillance using a correlation filter to identify the position of an object of interest in a current frame based on template frames from previous frames, even when the object is blocked or blurred.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single template frame is used for tracking, then the device complexity is reduced, but the tracking accuracy deteriorates due to uncertain motion state and shape variations of faces
Solution Approach 1:
The patent segments the template selection into multiple discrete template frames (at least two different template frames) instead of using a single template. Each template frame captures different features of the object of interest, allowing the system to handle shape variations and motion uncertainties by selecting from multiple templates rather than relying on one complex adaptive template.
Solution Approach 2:
The patent performs preliminary selection of multiple template frames from historical video data before the actual tracking occurs. These templates are pre-processed and stored, so when tracking is needed, the system can directly use these prepared templates without complex real-time adaptation, thus improving accuracy while controlling complexity.
2Reliability
If multiple template frames are used to improve tracking robustness, then the tracking accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent uses a limited number of template frames (at least two, but not an excessive number) to achieve the necessary tracking robustness. This partial action approach avoids the computational burden of using too many templates while still providing enough variation to handle different face shapes and motion states effectively.
3Measurement precision
If template features are selected to match specific face shapes, then the tracking accuracy improves for that shape, but the adaptability to different face shapes deteriorates
Solution Approach 1:
The patent creates a universal tracking solution by using multiple template frames that collectively represent different face shapes and features. Instead of creating specialized templates for each face shape (which would improve accuracy for specific shapes but reduce versatility), the system uses a set of multi-functional templates that can adapt to various face types, achieving both accuracy and adaptability.
Data Source
AI summary
Systems and methods for tracking an object in video surveillance are provided. A method may include obtaining a video including a plurality of consecutive frames; obtaining a current frame from the plurality of consecutive frames, wherein an object of interest is identified in at least two previous frames of the current frame; obtaining at least two template frames from the at least two previous frames; and identifying a position related to the object of interest in the current frame based on the at least two template frames using a correlation filter.