Related papers: MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method

MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method

URL: http://arxiv.org/abs/2005.09167v1
Date: Tue, 19 May 2020 02:18:01 GMT
Title: MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
Authors: Xixi Xu, Chao Lu, Liang Zhu, Xiangyang Xue, Guanxian Chen, Qi Guo, Yining Lin, Zhijian Zhao
Abstract summary: A new multi-target tracking system, named MOTS, is based on metrics but not limited to track specific category. With a newly built TRACK-REID data-set, the Fine-match Network can perform matching of 31 category targets, even generalizes to unseen categories.
Score: 38.009864162410615
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Most modern Multi-Object Tracking (MOT) systems typically apply REID-based paradigm to hold a balance between computational efficiency and performance. In the past few years, numerous attempts have been made to perfect the systems. Although they presented favorable performance, they were constrained to track specified category. Drawing on the ideas of few shot method, we pioneered a new multi-target tracking system, named MOTS, which is based on metrics but not limited to track specific category. It contains two stages in series: In the first stage, we design the self-Adaptive-matching module to perform simple targets matching, which can complete 88.76% assignments without sacrificing performance on MOT16 training set. In the second stage, a Fine-match Network was carefully designed for unmatched targets. With a newly built TRACK-REID data-set, the Fine-match Network can perform matching of 31 category targets, even generalizes to unseen categories.

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