Object Clustering in Specific Scene

Team Members

Shen-Chi Chen, Ko-Chun Lin, Ling-Erh Lan, Ke-Yun Lin


In this approach, we classified the objects inside the surveillance video into three categories: pedestrians, scooters, and vehicles. A period of video was derived to construct a specific model for this scene. According the perspective, a same object will have different information at different position: object size, moving direction, and moving velocity. On the other hand, different objects at the same position will have different information either, such as aspect ratio and object size. Based on this reason, we divided the scene into blocks, accumulating data with each kind of category, and construct three different models for them.
After the object classification, we develop a temporal-spatial quick browsing system for surveillance video to solve this problem. The user can not only browse the surveillance videos efficiently but also clearly look at the target while retrieving targets. Also, user can query a certain kind of category they want to see in the condensate surveillance video.