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In online multi-target tracking, modeling of appearance and geometricsimilarities between pedestrians visual scenes is of great importance. Thehigher dimension of inherent information in the appearance model compared tothe geometric model is problematic in many ways. However, due to the recentsuccess of deep-learning-based methods, handling of high-dimensional appearanceinformation becomes feasible. Among many deep neural networks, Siamese networkwith triplet loss has been widely adopted as an effective appearance featureextractor. Since the Siamese network can extract the features of each inputindependently, one can update and maintain target-specific features. However,it is not suitable for multi-target settings that require comparison with otherinputs. To address this issue, we propose a novel track appearance model basedon the joint-inference network. The proposed method enables a comparison of twoinputs to be used for adaptive appearance modeling and contributes to thedisambiguation of target-observation matching and to the consolidation ofidentity consistency. Diverse experimental results support the effectiveness ofour method. Our work was recognized as the 3rd-best tracker in BMTTMOTChallenge 2019, held at CVPR2019. The code is available athttps://github.com/yyc9268/Deep-TAMA.

Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association