Open AccessMultiple object tracking based on multi‐task learning with strip attention
Author(s) -
Song Yaoye,
Zhang Peng,
Huang Wei,
Zha Yufei,
You Tao,
Zhang Yanning
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12327
Subject(s) - computer science , discriminative model , robustness (evolution) , video tracking , artificial intelligence , feature extraction , benchmark (surveying) , object detection , computer vision , embedding , pattern recognition (psychology) , backbone network , boosting (machine learning) , machine learning , object (grammar) , computer network , biochemistry , chemistry , geodesy , gene , geography
Multiple object tracking (MOT) framework based on bifurcate strategy was usually challenged by data association of different model path, which work for object localisation and appearance embedding independently. By incorporating the re‐identification (re‐ID) as appearance embedding model, more recent studies on task combination of a single network have made a great progress in tracking performance. Unfortunately, the contributive improvement from re‐ID model is hard to balance the accuracy and efficiency for the whole framework. For more effective enhancement of the overall tracking performance, a real‐time detection needs to be taken into consideration with other auxiliary means for MOT modelling. Therefore, in this study, a one‐shot multiple object tracking is proposed based on multi‐task learning to obtain satisfactory performance in both speed and robustness. With updated re‐training strategy for the backbone model of detection, a D2LA network is proposed to achieve more characteristic fine‐grained feature extraction in branching task of pedestrian recognition. Additionally, a strip attention module is also introduced to further strengthen the feature discriminative capability of the tracking framework in occlusion. Experiments on the 2DMOT15, MOT16, MOT17, and MOT20 benchmark data sets have shown a superior performance in comparison to other state‐of‐the‐art tracking approaches.