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Correlation filters with convolutional neural network (CNN) features have been successfully applied to visual tracking owing to their impressive combined capability for object representation. Unfortunately, further performance improvement is limited due to unwanted boundary effects of the circular structure. In this work, through an in‐depth study of the features' characteristics, the authors propose a novel tracking strategy to achieve simultaneous filter matching and regularization with CNN features when tracking is on the fly. With a feature decomposed transform matrix, a spatial semantic regularization is generated to reduce the boundary effect effectively during filter optimization. Before each output, the regularized filter is then back performed to match with the extracted features of a search region to find the optimum candidate. Specifically, the most important advantage of the proposed spatial semantic map is to initialize only in the first frame as all the other tracking strategies. Besides, the authors design a novel updating strategy to tackle the cases where the object is occluded or disappeared in the scene. At this time, the maximum of the map is small, even negative. A substantial experiment has been carried out on the popular benchmark tracking datasets; the reliable results have demonstrated that the authors' method is able to outperform most of the state‐of‐the‐art tracking works in both accuracy and robustness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Tracking based on correlation filters has demonstrated outstanding performance in recent visual object tracking studies and competitions. However, the performance is limited since the boundary effects are introduced by the intrinsic circular structure. In this study, a tracker, called adversarial correlation filter tracker (ACFT), is proposed to solve the above problem through Generative Adversarial Networks (GANs) that is specifically strong at producing realistic‐looking data from noise circumstances. Especially, a mask is generated by the GANs to assist the conventional correlation filter for the spatial regularisation. By overcoming the feature independence of current regularisation in another tracker, the GANs' mask can be effectively used to identify the robust features for the target variations representation in the temporal domain. Also in the spatial domain, the background features can be substantially suppressed to obtain the optimisation filter for more reliable matching and updating. In verification, the authors evaluate the proposed tracker on the standard tracking benchmarks, and the experimental results show that their tracker outperforms favourably against other state‐of‐the‐art trackers in the measurements of accuracy and robustness. [ABSTRACT FROM AUTHOR]