Multiple Object Tracking from appearance by hierarchically clustering tracklets

• URL: http://arxiv.org/abs/2210.03355v1
• Date: Fri, 7 Oct 2022 07:04:15 GMT
• Title: Multiple Object Tracking from appearance by hierarchically clustering tracklets
• Authors: Andreu Girbau, Ferran Marqu\'es, Shin'ichi Satoh
• Abstract summary: Current approaches in Multiple Object Tracking rely on the-temporal coherence between detections combined with object appearance to match objects from consecutive frames. In this work, we explore using object appearances as the main source of association between objects in a video, using spatial and temporal priors weighting factors. We form initial tracklets by leveraging on the idea that instances of an object that are close in time should be similar in appearance, and build the final object tracks by the tracklets in a hierarchical fashion.
• Score: 16.65329510916639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current approaches in Multiple Object Tracking (MOT) rely on the spatio-temporal coherence between detections combined with object appearance to match objects from consecutive frames. In this work, we explore MOT using object appearances as the main source of association between objects in a video, using spatial and temporal priors as weighting factors. We form initial tracklets by leveraging on the idea that instances of an object that are close in time should be similar in appearance, and build the final object tracks by fusing the tracklets in a hierarchical fashion. We conduct extensive experiments that show the effectiveness of our method over three different MOT benchmarks, MOT17, MOT20, and DanceTrack, being competitive in MOT17 and MOT20 and establishing state-of-the-art results in DanceTrack.

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