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• W2889767951 abstract "Vision-based object tracking approach has been drawing increased attention from both the academia and the industry in recent years. One of its most successful applications is the monitoring system, which can be installed on infrastructures or on mobile platforms (e.g., vehicles) to track pedestrians or bicyclists in a pre-defined region and further to prevent probable accidents. Despite tremendous progress achieved, the task of visual tracking is still challenging, especially in dealing with severe occlusions, where the tracker may fail due to the abrupt change of object appearance. Such case is common not only in traffic scenarios but also in other tracking tasks. Aiming to tackle this problem, in this paper, we propose a new tracking approach by adopting part based trackers, which are built in the form of correlation filter. In this approach, the occluded object parts are identified by leveraging the knowledge derived from image features and filter responses. With the help of a masking process, visible object areas are acquired in a pixel-wise precision and utilized to build part filters. As both the number and size of part filters are adapted to the current object appearance, the influence of occlusions can be significantly suppressed. Experimental results on traffic sequences demonstrate that our tracker performs robust against occlusion, especially in cases, where long term and severe occlusions appear. A further experiment on the standard benchmark proves that our approach outperforms state-of-the-art methods in tracking various object classes under varied circumstances. Furthermore, the proposed tracker is sophisticatedly designed and is feasible for real time applications." @default.
• W2889767951 created "2018-09-27" @default.
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• W2889767951 date "2018-01-01" @default.
• W2889767951 modified "2023-09-24" @default.
• W2889767951 title "Tracking Objects with Severe Occlusion by Adaptive Part Filter Modeling - In Traffic Scenes and Beyond" @default.
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• W2889767951 doi "https://doi.org/10.1109/mits.2018.2867517" @default.
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