appearance based retrieval for tracked objects in surveillance videos
This paper focuses on indexing and retrieval at the object level for video surveillance. Object retrieval is difficult due to imprecise object detection and tracking. In the indexing phase, a new representative blob detection method allows to choose the most relevant blobs that represent various object's visual aspects. In the retrieval phase, a new robust object matching method retrieves successfully objects even though they are not perfectly tracked. We validate our approach thanks to videos coming from a subway monitoring project. The representative blob detection method improves the state of the art. The obtained retrieval results show that the object matching method is robust while working with imprecise object tracking algorithms.
surveillance video object matching video retrieval