Track Aware Object Detection Bounding Box Selection
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Solution Overview
Problem
Existing object tracking systems select bounding boxes without considering previous frames, leading to reduced accuracy due to the elimination of necessary bounding boxes and selection of inappropriate ones.
Innovation Solution
Implement a track-aware object detection system that uses an image processor to identify frames, create candidate bounding boxes, compare them to predicted bounding boxes based on current tracks, and adjust confidence scores to ensure accurate tracking, while utilizing a universal embedding model for vector embeddings and smart clustering techniques to maintain object perspectives and determine track persistence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection selects bounding boxes independently for each frame without considering previous frames, then the processing speed is maintained, but the tracking accuracy deteriorates due to elimination of necessary bounding boxes and selection of inappropriate ones
Solution Approach 1:
The system performs preliminary actions by generating predicted bounding boxes based on previous track information before the actual detection step. This prediction is done using the last known position and velocity of the object, allowing the system to prepare expected locations in advance and compare them with detected bounding boxes, thereby improving tracking accuracy without significantly increasing overall system complexity
Solution Approach 2:
The system implements feedback by using the detected bounding boxes from previous frames to inform and adjust the selection in current frames. The track information is continuously updated and fed back into the detection process, creating a closed-loop system where past detection results influence future selections, thereby maintaining high tracking accuracy across frames
2Measurement precision
If track-aware detection with predicted bounding boxes and confidence score adjustment is implemented, then tracking accuracy is improved, but computational complexity increases
Solution Approach 1:
The system changes parameters by dynamically adjusting confidence score thresholds based on track quality metrics such as IoU (Intersection over Union) with predicted bounding boxes and temporal consistency measures. This adaptive parameter adjustment allows the system to maintain high detection accuracy while avoiding unnecessary computational operations when track confidence is already high, thereby balancing accuracy improvements with computational efficiency
3Reliability
If multiple candidate bounding boxes are evaluated and confidence scores are adjusted based on track information, then the reliability of object tracking is improved, but the processing time increases
Solution Approach 1:
The system applies partial action by selectively performing full confidence score adjustment and re-evaluation only for candidate bounding boxes that are close to the predicted position (within a certain IoU threshold). For candidates that are clearly mismatched or far from predicted locations, the system uses simpler rejection criteria, thereby maintaining high tracking reliability for relevant objects while reducing processing time through selective detailed evaluation
Data Source
AI summary
Examples of the present disclosure describe systems and methods for track aware object detection. In examples, image content comprising one or more objects is received. Frames in the image content are identified. Candidate bounding boxes are created around objects to be tracked in the frames and a confidence score is assigned to each candidate bounding box. The candidate bounding boxes for each object are compared to a predicted bounding box that is generated based on a current track for the object. Candidate bounding boxes that are determined to be similar to the predicted bounding box and/or that exceed a confidence score threshold are selected. The selected candidate bounding boxes are filtered until a single candidate bounding box that is most representative of each object to be tracked remains. The frame comprising the representative bounding box for each object is then added to a current track for the object.