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In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve satisfactory tracking performance. In this paper, a new end-to-end multi-object tracking framework is proposed, which integrates object detection and multi-object tracking into a single model. The proposed tracking framework eliminates the complex data association process in the classical TBD paradigm, and requires no additional training. Secondly, the regression confidence of historical trajectories is investigated, and the possible states of a trajectory (weak object or strong object) in the current frame are predicted. Then, a confidence fusion module is designed to guide non-maximum suppression for trajectories and detections to achieve ordered and robust tracking. Thirdly, by integrating historical trajectory features, the regression performance of the detector is enhanced, which better reflects the occlusion and disappearance patterns of objects in real world. Lastly, extensive experiments are conducted on the commonly used KITTI and Waymo datasets. The results show that the proposed framework can achieve robust tracking by using only a 2D detector and a 3D detector, and it is proven more accurate than many of the state-of-the-art TBD-based multi-modal tracking methods. The source codes of the proposed method are available at https://github.com/wangxiyang2022/YONTD-MOT., Comment: 11 pages, 7 figures

In currently available literature, no tracking-by-detection (TBD) paradigm-based tracking method has considered the localization confidence of detection boxes. In most TBD-based methods, it is considered that objects of low detection confidence are highly occluded and thus it is a normal practice to directly disregard such objects or to reduce their priority in matching. In addition, appearance similarity is not a factor to consider for matching these objects. However, in terms of the detection confidence fusing classification and localization, objects of low detection confidence may have inaccurate localization but clear appearance; similarly, objects of high detection confidence may have inaccurate localization or unclear appearance; yet these objects are not further classified. In view of these issues, we propose Localization-Guided Track (LG-Track). Firstly, localization confidence is applied in MOT for the first time, with appearance clarity and localization accuracy of detection boxes taken into account, and an effective deep association mechanism is designed; secondly, based on the classification confidence and localization confidence, a more appropriate cost matrix can be selected and used; finally, extensive experiments have been conducted on MOT17 and MOT20 datasets. The results show that our proposed method outperforms the compared state-of-art tracking methods. For the benefit of the community, our code has been made publicly at https://github.com/mengting2023/LG-Track., Comment: 11 pages, 4 figures

Yang Song,1,2 Dake Hao,3 Huan Jiang,4 Mingguang Huang,2 Qingjun Du,2 Yi Lin,2 Fei Liu,1 Bin Chen1 1Division of Orthopaedics and Traumatology, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 2Division of Traumatology and Joint, Department of Orthopaedics, Shunde Hospital, Southern Medical University, Foshan, 528308, People’s Republic of China; 3Department of Surgery, School of Medicine, University of California Davis, Sacramento, CA, 95817, USA; 4Department of Anesthesiology, Shunde Hospital, Southern Medical University, Foshan, 528308, People’s Republic of ChinaCorrespondence: Bin ChenDivision of Orthopaedics and Traumatology, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People’s Republic of ChinaEmail chb@smu.edu.cnIntroduction: Post-traumatic osteoarthritis (PTOA) is an inflammatory condition that occurs following mechanical joint trauma and that results in joint degeneration. This study sought to evaluate the regulatory function of nuclear factor erythroid 2-related factor 2 (Nrf2) in a murine model of anterior cruciate ligament transection (ACLT)-induced PTOA and in an in vitro model of synoviocyte inflammation induced by LPS treatment with the goal of exploring the role of chitinase 3-like-1 (CHI3L1) in this pathogenic context.Methods: PTOA model mice were intra-articularly injected with Nrf2 overexpression lentiviral vector, and safranin O-fast green staining as well as the Osteoarthritis Research Society International (OARSI) Scoring System were used to evaluate the severity of cartilage damage. Protein expression in the synovial tissue was evaluated by Western blotting, immunohistochemical staining, and ELISA. Additionally, murine synoviocytes were infected with Nrf2 overexpression lentivirus and stimulated with LPS. The levels of inflammatory cytokines were detected by ELISA. ROS levels were measured using dihydroethidium (DHE)