Incident Retrieval in Transportation Surveillance Videos - An Interactive Framework
Xin Chen, Chengcui Zhang · 2007
Detecting and retrieving incidents from traffic surveillance videos is an important research topic in designing an Intelligent Transportation System (ITS). Most existing video analysis techniques focus on low level features of video data. A "semantic gap" exists between the machine-readable low level features and the high level human understanding of the video content. Aiming at this problem, we propose an interactive framework for semantic video retrieval. This framework is based on the spatio-temporal modeling of vehicle trajectories. With Relevance Feedback (RF), human interaction is involved in the learning and retrieval process. The retrieval mechanism is thus guided by the user's response to the retrieved results. Experiments show the effectiveness of the framework.