Multi-object tracking with self-supervised associating network

• URL: http://arxiv.org/abs/2010.13424v1
• Date: Mon, 26 Oct 2020 08:48:23 GMT
• Title: Multi-object tracking with self-supervised associating network
• Authors: Tae-young Chung, Heansung Lee, Myeong Ah Cho, Suhwan Cho, Sangyoun Lee
• Abstract summary: We propose a novel self-supervised learning method using a lot of short videos which has no human labeling. Despite the re-identification network is trained in a self-supervised manner, it achieves the state-of-the-art performance of MOTA 62.0% and IDF1 62.6% on the MOT17 test benchmark.
• Score: 5.947279761429668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-Object Tracking (MOT) is the task that has a lot of potential for development, and there are still many problems to be solved. In the traditional tracking by detection paradigm, There has been a lot of work on feature based object re-identification methods. However, this method has a lack of training data problem. For labeling multi-object tracking dataset, every detection in a video sequence need its location and IDs. Since assigning consecutive IDs to each detection in every sequence is a very labor-intensive task, current multi-object tracking dataset is not sufficient enough to train re-identification network. So in this paper, we propose a novel self-supervised learning method using a lot of short videos which has no human labeling, and improve the tracking performance through the re-identification network trained in the self-supervised manner to solve the lack of training data problem. Despite the re-identification network is trained in a self-supervised manner, it achieves the state-of-the-art performance of MOTA 62.0\% and IDF1 62.6\% on the MOT17 test benchmark. Furthermore, the performance is improved as much as learned with a large amount of data, it shows the potential of self-supervised method.

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