Event Detection and Analysis from Video Streams
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A System for Learning Statistical Motion Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A differential geometric approach to representing the human actions
Computer Vision and Image Understanding
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploratory Search
IEEE Transactions on Circuits and Systems for Video Technology
Content-Based Video Retrieval (CBVR) System for CCTV Surveillance Videos
DICTA '09 Proceedings of the 2009 Digital Image Computing: Techniques and Applications
Visual event recognition using decision trees
Multimedia Tools and Applications
Content-based retrieval of video surveillance scenes
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
Joint Key-Frame Extraction and Object Segmentation for Content-Based Video Analysis
IEEE Transactions on Circuits and Systems for Video Technology
High-Speed Action Recognition and Localization in Compressed Domain Videos
IEEE Transactions on Circuits and Systems for Video Technology
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We present a fast and flexible content-based retrieval method for surveillance video. Designing a video search robust to uncertain activity duration, high variability in object shapes and scene content is challenging. We propose a two-step approach to video search. First, local features are inserted into an inverted index using locality-sensitive hashing (LSH). Second, we utilize a novel dynamic programming (DP) approach to robustify against temporal distortion, limited obscuration and imperfect queries. DP exploits causality to assemble the local features stored in the index into a video segment which matches the query video. Pre-processing of archival video is performed in real-time, and retrieval speed scales as a function of the number of matches rather than video length. We derive bounds on the rate of false positives, demonstrate the effectiveness of the approach for counting, motion pattern recognition and abandoned object applications using seven challenging video datasets and compare with existing work.