  In the domain of video surveillance, describing the behavior of each
individual within the video is becoming increasingly essential, especially in
complex scenarios with multiple individuals present. This is because describing
each individual's behavior provides more detailed situational analysis,
enabling accurate assessment and response to potential risks, ensuring the
safety and harmony of public places. Currently, video-level captioning datasets
cannot provide fine-grained descriptions for each individual's specific
behavior. However, mere descriptions at the video-level fail to provide an
in-depth interpretation of individual behaviors, making it challenging to
accurately determine the specific identity of each individual. To address this
challenge, we construct a human-centric video surveillance captioning dataset,
which provides detailed descriptions of the dynamic behaviors of 7,820
individuals. Specifically, we have labeled several aspects of each person, such
as location, clothing, and interactions with other elements in the scene, and
these people are distributed across 1,012 videos. Based on this dataset, we can
link individuals to their respective behaviors, allowing for further analysis
of each person's behavior in surveillance videos. Besides the dataset, we
propose a novel video captioning approach that can describe individual behavior
in detail on a person-level basis, achieving state-of-the-art results. To
facilitate further research in this field, we intend to release our dataset and
code.
