  In online multi-target tracking, modeling of appearance and geometric
similarities between pedestrians visual scenes is of great importance. The
higher dimension of inherent information in the appearance model compared to
the geometric model is problematic in many ways. However, due to the recent
success of deep-learning-based methods, handling of high-dimensional appearance
information becomes feasible. Among many deep neural networks, Siamese network
with triplet loss has been widely adopted as an effective appearance feature
extractor. Since the Siamese network can extract the features of each input
independently, one can update and maintain target-specific features. However,
it is not suitable for multi-target settings that require comparison with other
inputs. To address this issue, we propose a novel track appearance model based
on the joint-inference network. The proposed method enables a comparison of two
inputs to be used for adaptive appearance modeling and contributes to the
disambiguation of target-observation matching and to the consolidation of
identity consistency. Diverse experimental results support the effectiveness of
our method. Our work was recognized as the 3rd-best tracker in BMTT
MOTChallenge 2019, held at CVPR2019. The code is available at
https://github.com/yyc9268/Deep-TAMA.
