Focus-MOT: Multi-target tracking detection algorithm with fine-grained feature extraction aggregation

This work proposes a multi-target tracking and detection algorithm Focus-MOT based on feature refinement extraction fusion, t through the designed Field Enhancement Refinement Module and Information Aggregation Module, which effectively reduces the number of target ID switching.Jointly learns the Detector and Embedding model method becomes the mainstream of multi-target tracking and detection due to its fast detection speed, its Re-ID branch needs to use low-dimensional features and high-dimensional features to accommodate both large and small targets, however, its insufficient feature extraction leads to high ID_SW. Therefore this work aims to extract features of different levels for aggregation as a way to reduce the number of ID switching. The experimental results show a 2.7% improvement in MOTA and a 2300 times decrease in ID_SW relative to the results of the FairMOT algorithm on the MOT17 dataset.