Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer

• URL: http://arxiv.org/abs/2211.05654v1
• Date: Wed, 9 Nov 2022 07:19:33 GMT
• Title: Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer
• Authors: Siddharth Sagar Nijhawan, Leo Hoshikawa, Atsushi Irie, Masakazu Yoshimura, Junji Otsuka, Takeshi Ohashi
• Abstract summary: We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking. It is driven by a transformer based backbone instead of CNN, which is highly scalable with the input resolution. As a result of our modifications, we reduce the overall model size of TransTrack by 58.73% and the complexity by 78.72%.
• Score: 0.8808021343665321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking using a fully-transformer architecture. It is a modified version of TransTrack, which overcomes the computational bottleneck associated with its design, and at the same time, achieves state-of-the-art MOTA score of 73.20%. The model design is driven by a transformer based backbone instead of CNN, which is highly scalable with the input resolution. We also propose a drop-in replacement for Feed Forward Network of transformer encoder layer, by using Butterfly Transform Operation to perform channel fusion and depth-wise convolution to learn spatial context within the feature maps, otherwise missing within the attention maps of the transformer. As a result of our modifications, we reduce the overall model size of TransTrack by 58.73% and the complexity by 78.72%. Therefore, we expect our design to provide novel perspectives for architecture optimization in future research related to multi-object tracking.

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