Video Target Tracking with Dynamic Collation Range Adjustment
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Solution Overview
Problem
Existing person tracking techniques struggle with accuracy when objects cross obstacles or are captured by multiple cameras, leading to misidentification and reduced tracking precision over wide ranges.
Innovation Solution
A tracking device that adjusts collation ranges based on user-designated ranges and exclusion zones, using spatial and temporal thresholds to maintain consistent tracking across image frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed collation range is used for tracking, then the tracking process is simple, but tracking accuracy decreases when objects cross obstacles or are captured by multiple cameras
Solution Approach 1:
The collation range is dynamically adjusted based on the number of detected objects and their spatial relationships. When objects are densely distributed, the collation range is reduced to avoid misidentification. When objects are sparsely distributed, the collation range is expanded to maintain tracking across obstacles and camera boundaries. This dynamic adjustment resolves the contradiction by adapting the complexity of the tracking process to the actual scene conditions.
Solution Approach 2:
The system changes the collation range parameter according to the distribution density of tracking targets. By modifying this key parameter based on detected object characteristics, the system achieves high tracking accuracy in both simple and complex scenarios without requiring overly complicated processing in all cases.
2Measurement precision
If a small collation range is used, then misidentification between different objects is reduced, but tracking is lost when objects cross obstacles
Solution Approach 1:
The collation range dynamically adapts to the spatial distribution of objects. In dense regions where misidentification risk is high, the range shrinks to improve identification accuracy. In sparse regions or when objects approach obstacles, the range expands to ensure tracking continuity. This dynamic behavior resolves the contradiction between precision and reliability.
Solution Approach 2:
Different collation ranges are applied to different spatial regions based on object density. Local adjustments allow the system to maintain tight collation ranges near dense object groups to prevent misidentification, while using larger ranges in open spaces to maintain tracking across obstacles, thus achieving both precision and reliability locally.
3Reliability
If a large collation range is used, then tracking continuity is maintained across obstacles, but misidentification between different objects increases
Solution Approach 1:
The system dynamically reduces the collation range when multiple objects are detected in close proximity to prevent misidentification, while maintaining larger ranges when objects are isolated to ensure tracking continuity. This dynamic adjustment resolves the contradiction by responding to real-time spatial conditions.
Solution Approach 2:
The collation range parameter is changed based on the detected number and distribution of objects. When object density increases, the parameter is reduced to improve identification accuracy. When density decreases, the parameter is increased to maintain tracking reliability, thus resolving the precision-reliability contradiction.
4Measurement precision
If dynamic adjustment of collation range is implemented, then tracking accuracy improves in complex scenes, but processing complexity increases
Solution Approach 1:
The system adjusts the collation range parameter based on simple metrics such as the number of detected objects and their spatial distribution. By using straightforward parameter changes rather than complex algorithms, the system achieves high tracking accuracy in complex scenes while keeping processing complexity manageable.
Solution Approach 2:
The dynamic adjustment focuses computational resources on local regions where objects are densely distributed or where tracking challenges are detected. This localized approach improves tracking accuracy in complex areas while avoiding unnecessary processing in simple regions, thus managing overall processing complexity.
Data Source
AI summary
A tracking device including a designation range acquisition unit that acquires a designation range designated for each of a plurality of image frames constituting video data, a tracking unit that extracts the image frame to be verified from the video data, detect a tracking target for each of the extracted image frames, set a collation range for the detected tracking target, and adjust the collation range based on the designation range set for each of the image frames, and a display information generation unit that generates, for each of the image frames, a tracking image in which the collation range is associated with the tracking target.