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This paper presents a new approach to multi-target tracking in crowded scenes using a HybridBoosted method. It addresses the challenges posed by varying lengths of ground truth tracklets and proposes solutions to improve classification accuracy using strong and weak ranking classifiers. The study discusses sample weight updates and feature quantization techniques to optimize performance. Key components include human detection, ground truth construction, and association demonstration, culminating in significant advancements in tracking efficiency and robustness in complex environments.
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Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene Present by 陳群元
Strong ranking classifier Strong ranking classifier weak weak weak weak Update sample weight Update weight Update weight
previous problem • The lengths of ground truth tracklets are equal. tracklet tracklet tracklet tracklet Low Z value tracklet
solution • Cut trajectory to tracklet randomly tracklet tracklet tracklet tracklet
Previous problem • The scales of some thresholds are wide. Feature 1 Feature 5
solution • Quantize these features’ threshold with respective bins. • Quantize the difference of min and max value with difference bin.
To do • Human detection • Build ground truth • association
The end • Thank you!
