  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.
