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• W4221158054 abstract "Activity detection in surveillance videos is a challenging task caused by small objects, complex activity categories, its untrimmed nature, etc. Existing methods are generally limited in performance due to inaccurate proposals, poor classifiers or inadequate post-processing method. In this work, we propose a comprehensive and effective activity detection system in untrimmed surveillance videos for person-centered and vehicle-centered activities. It consists of four modules, i.e., object localizer, proposal filter, activity classifier and activity refiner. For person-centered activities, a novel part-attention mechanism is proposed to explore detailed features in different body parts. As for vehicle-centered activities, we propose a localization masking method to jointly encode motion and foreground attention features. We conduct experiments on the large-scale activity detection datasets VIRAT, and achieve the best results for both groups of activities. Furthermore, our team won the 1st place in the TRECVID 2021 ActEV challenge." @default.
• W4221158054 created "2022-04-03" @default.
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• W4221158054 date "2022-07-18" @default.
• W4221158054 modified "2023-09-27" @default.
• W4221158054 title "PAMI-AD: An Activity Detector Exploiting Part-Attention and Motion Information in Surveillance Videos" @default.
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• W4221158054 doi "https://doi.org/10.1109/icmew56448.2022.9859481" @default.
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