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We propose a light-weight and highly efficient Joint Detection and Trackingpipeline for the task of Multi-Object Tracking using a fully-transformerarchitecture. It is a modified version of TransTrack, which overcomes thecomputational 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 atransformer based backbone instead of CNN, which is highly scalable with theinput resolution. We also propose a drop-in replacement for Feed ForwardNetwork of transformer encoder layer, by using Butterfly Transform Operation toperform channel fusion and depth-wise convolution to learn spatial contextwithin the feature maps, otherwise missing within the attention maps of thetransformer. As a result of our modifications, we reduce the overall model sizeof TransTrack by 58.73% and the complexity by 78.72%. Therefore, we expect ourdesign to provide novel perspectives for architecture optimization in futureresearch related to multi-object tracking.

Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer