AFMtrack: Attention-Based Feature Matching for Multiple Object Tracking

AFMtrack: Attention-Based Feature Matching for Multiple Object Tracking

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Real-time multiple object tracking plays a pivotal role in autonomous driving applications, particularly in real-world applications.Current methods in various domains often face an inherent trade-off between accuracy and speed.This dilemma arises from the need to achieve high precision, which tends to entail the development of more intricate models.However, these complex models often incur a high processing overhead.

In this study, we introduced an Attention-based Feature Matching tracker for multiple-object tracking, named AFMTrack.We developed AFMTrack as a Transformer-based tracking method that improves click here speed and accuracy to provide a comprehensive solution to real-time tracking challenges.In particular, we designed a feature-matching module, which is a multi-layer attention-based network, to produce an association matrix by learning the correspondence between frames.Furthermore, in simple scenarios, we realized that a few layers were sufficient to attain the desired accuracy.

Therefore, an Early stopping mechanism is added to halt the process when early layers produce confident predictions.This significantly accelerated AFMTrack rubbermaid 8 gallon trash can without compromising accuracy.The effectiveness of AFMTrack is proven with 89.64% MOTA, 132 FPS on the KITTI dataset, and 79.

3% MOTA, 31 FPS on MOT17 dataset, surpassing most of methods on the leaderboards.The evaluations provide solid evidence that AFMTrack excels in multiple-object tracking, achieving state-of-the-art performance in this domain.