Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model

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Abstract

We address the problem of multi-target tracking in realistic crowded conditions by introducing a novel dual-stage online tracking algorithm. The problem of data-association between tracks and detections, based on appearance, is often complicated by partial occlusion. In the first stage, we address the issue of occlusion with a novel method of robust data-association, that can be used to compute the appearance similarity between tracks and detections without the need for explicit knowledge of the occluded regions. In the second stage, broken tracks are linked based on motion and appearance, using an online-learned linking model. The online-learned motion-model for track linking uses the confident tracks from the first stage tracker as training examples. The new approach has been tested on the town centre dataset and has performance comparable with the present state-of-the-art
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance
Pages37-42
Number of pages6
DOIs
Publication statusPublished - Aug 2013
EventThe IEEE International Conference on Advanced Video and Signal Based Surveillance 2013 - Krakov, Poland
Duration: 27 Aug 201330 Aug 2013

Conference

ConferenceThe IEEE International Conference on Advanced Video and Signal Based Surveillance 2013
CountryPoland
CityKrakov
Period27/08/201330/08/2013

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  • Cite this

    McLaughlin, N., Martinez-del-Rincon, J., & Miller, P. (2013). Online Multiperson Tracking With Occlusion Reasoning and Unsupervised Track Motion Model. In Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance (pp. 37-42) https://doi.org/10.1109/AVSS.2013.6636613