The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
Video-Based Surveillance Systems: Computer Vision and Distributed Processing
The Truth about Corel - Evaluation in Image Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Multimedia surveillance: content-based retrieval with multicamera people tracking
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
ETISEO, performance evaluation for video surveillance systems
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Video sequence querying using clustering of objects' appearance models
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
A query language combining object features and semantic events for surveillance video retrieval
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Object-based surveillance video retrieval system with real-time indexing methodology
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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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.