Object Tracking via Selective Candidate Region Verification
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Solution Overview
Problem
Existing systems face challenges in achieving real-time object tracking and detection in electronic devices due to high computational demands, often losing track of objects, especially in complex scenarios and varying image processing requirements.
Innovation Solution
The proposed method optimizes object tracking and detection by employing techniques such as selective scanning windows, limited spatial scales, confidence-based image search, sequential tracking and detection, and dynamic adjustment of detection windows, reducing computational load and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive object detection and tracking algorithms are implemented, then detection accuracy and reliability are improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the object tracking process into multiple stages: candidate region generation, candidate region verification, and final tracking. This segmentation allows the system to process only relevant regions rather than entire images, reducing computational load while maintaining tracking reliability through staged verification.
Solution Approach 2:
The patent applies partial action by performing detection only on selected candidate regions rather than exhaustive full-image scanning. The system generates candidate regions based on motion cues and other constraints, then verifies only those candidates, avoiding unnecessary computational expenditure on regions unlikely to contain targets.
2Measurement precision
If full image scanning and comprehensive detection are performed, then detection precision is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by generating candidate regions and applying coarse filtering before detailed detection. Motion estimation and other preliminary processing steps identify likely target locations in advance, allowing the system to focus detailed detection algorithms only on those pre-selected regions, thereby maintaining precision while reducing overall processing time.
Solution Approach 2:
The detection process is segmented into coarse filtering and fine detection stages. Candidate regions are first identified using computationally efficient methods, then verified using more precise but computationally intensive algorithms only on those candidates, achieving high detection precision without exhaustive full-image processing.
3Reliability
If multiple detection algorithms and verification steps are used, then tracking reliability is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex tracking system into modular functional blocks: candidate region generation, verification, and tracking modules. Each module performs a specific function with well-defined inputs and outputs, making the overall complex system more manageable and maintainable while achieving high reliability through the coordinated operation of these specialized components.
Solution Approach 2:
The system applies multiple detection algorithms and verification steps selectively rather than universally. Different verification methods are applied based on the specific characteristics of candidate regions and tracking conditions, reducing unnecessary computational steps while maintaining reliability through targeted application of appropriate verification techniques.
Data Source
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AI summary
A method performed by an electronic device is described. The method includes obtaining a first frame of a scene. The method also includes performing object recognition of at least one object within a first bounding region of the first frame. The method further includes performing object tracking of the at least one object within the first bounding region of the first frame. The method additionally includes determining a second bounding region of a second frame based on the object tracking. The second frame is subsequent to the first frame. The method also includes determining whether the second bounding region is valid based on a predetermined object model.