Object-Based Surveillance Video Retrieval System with Real-Time Indexing Methodology

This paper presents a novel surveillance video indexing and retrieval system based on object features similarity measurement. The system firstly extracts moving objects from the videos by an efficient motion segmentation method. The fundamental features of each moving object are then extracted and indexed into the database. During retrieval, the system matches the query with the features indexed in the database without re-processing the videos. Video clips which contain the objects with sufficiently high relevance scores are then returned. The novelty of the system includes: 1. A real-time automatic indexing methodology achieved by a fast motion segmentation, such that the system is able to perform on-the-fly indexing on video sources; and 2. an object-based retrieval system with fundamental features matching approach, which allows user to specify the query by providing an example image or even a sketch of the desired objects. Such an approach can search the desired video clips in a more convenient and unambiguous way comparing with traditional text-based matching.