Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Real-time video-shot detection for scene surveillance applications
IEEE Transactions on Image Processing
Object-based video abstraction for video surveillance systems
IEEE Transactions on Circuits and Systems for Video Technology
Digital Video Event Detector Framework for Surveillance Applications
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Exploratory search of long surveillance videos
Proceedings of the 20th ACM international conference on Multimedia
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A novel method for content-based retrieval of surveillance video data is presented. The study starts from the realistic assumption that the automatic feature extraction is kept simple, i.e. only segmentation and low-cost filtering operations have been applied. The solution is based on a new and generic dissimilarity measure for discriminating video surveillance scenes. This weighted compound measure can be interactively adapted during a session in order to capture the user's subjectivity. Upon this, a key-frame selection and a content-based retrieval system have been developed and tested on several actual surveillance sequences. Experiments have shown how the proposed method is efficient and robust to segmentation errors.