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In the domain of video surveillance, describing the behavior of eachindividual within the video is becoming increasingly essential, especially incomplex scenarios with multiple individuals present. This is because describingeach individual's behavior provides more detailed situational analysis,enabling accurate assessment and response to potential risks, ensuring thesafety and harmony of public places. Currently, video-level captioning datasetscannot provide fine-grained descriptions for each individual's specificbehavior. However, mere descriptions at the video-level fail to provide anin-depth interpretation of individual behaviors, making it challenging toaccurately determine the specific identity of each individual. To address thischallenge, we construct a human-centric video surveillance captioning dataset,which provides detailed descriptions of the dynamic behaviors of 7,820individuals. Specifically, we have labeled several aspects of each person, suchas location, clothing, and interactions with other elements in the scene, andthese people are distributed across 1,012 videos. Based on this dataset, we canlink individuals to their respective behaviors, allowing for further analysisof each person's behavior in surveillance videos. Besides the dataset, wepropose a novel video captioning approach that can describe individual behaviorin detail on a person-level basis, achieving state-of-the-art results. Tofacilitate further research in this field, we intend to release our dataset andcode.

Human-centric Behavior Description in Videos: New Benchmark and Model