METFormer: a motion enhanced transformer for multiple object tracking

Abstract

Multiple object tracking (MOT) is an important task in computer vision, especially video analytics. Transformer-based methods are emerging approaches using both tracking and detection queries. However, motion modeling in existing transformer-based methods lacks effective association capability. Thus, this paper introduces a new METFormer model, a Motion Enhanced TransFormer-based tracker with a novel global-local motion context learning technique to mitigate the lack of motion information in existing transformer-based methods. The global-local motion context learning technique first centers on difference-guided global motion learning to obtain temporal information from adjacent frames. Based on global motion, we leverage context-aware local object motion modelling to study motion patterns and enhance the feature representation for individual objects. Experimental results on the benchmark MOT17 dataset show that our proposed method can surpass the state-of-the-art Trackformer [21] by 1.8% on IDF1 and 21.7% on ID Switches under public detection settings.