Success Rate: 67.33400Precision Score: 86.84020
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Object Tracking
Object tracking is the task of taking an initial set of object detections, creating a unique ID for each of the initial detections, and then tracking each of the objects as they move around frames in a video, maintaining the ID assignment. State-of-the-art methods involve fusing data from RGB and event-based cameras to produce more reliable object tracking. CNN-based models using only RGB images as input are also effective. The most popular benchmark is OTB. There are several evaluation metrics specific to object tracking, including HOTA, MOTA, IDF1, and Track-mAP.
<span style="color:grey; opacity: 0.6">( Image credit: Towards-Realtime-MOT )</span>
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Dataset기록된 모델 예시MetricPaperCode
데이터셋
93개논문
Success Rate: 65.0Precision Rate: 73.8
3DMOTA: 95
3DMOTA: 94.8
Average IOU: 0.66
Success Rate: 62.8Precision Rate: 71.4
Precision Plot: 0.627
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