论文标题
有效的关节检测和具有空间意识到变压器的多个对象跟踪
Efficient Joint Detection and Multiple Object Tracking with Spatially Aware Transformer
论文作者
论文摘要
我们建议使用完全转换器体系结构进行多对象跟踪的任务，提出轻巧且高效的关节检测和跟踪管道。这是TransTrack的修改版本，它克服了与其设计相关的计算瓶颈，同时达到了最新的MOTA得分为73.20％。该模型设计是由基于变压器的骨干而不是CNN驱动的，CNN可以通过输入分辨率高度扩展。我们还建议通过使用Butterfly Transform操作执行通道融合和深度卷积，以在特征图中学习空间上下文，否则在变压器的注意图中缺少了butterfly fression formister lase的馈送替换。由于我们的修改，我们将TransTrack的总体模型大小降低了58.73％，复杂性降低了78.72％。因此，我们希望我们的设计在与多对象跟踪有关的未来研究中为建筑优化提供新的观点。
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.
