Derivation of optical flow using a spatiotemporal-frequency approach
Computer Vision, Graphics, and Image Processing
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Hidden Markov Models for Optical Flow Analysis in Crowds
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Color aided motion-segmentation and object tracking for video sequences semantic analysis: Articles
International Journal of Imaging Systems and Technology - Special Issue on Applied Color Image Processing
Ontology-Based Trajectory Analysis for Semantic Event Detection
ICSC '07 Proceedings of the International Conference on Semantic Computing
Recursive estimation of time-varying motion and structure parameters
Pattern Recognition
Real-Time crowd density estimation using images
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
A generalized change detection problem
IEEE Transactions on Information Theory
Efficient, robust, and fast global motion estimation for video coding
IEEE Transactions on Image Processing
Shot-boundary detection: unraveled and resolved?
IEEE Transactions on Circuits and Systems for Video Technology
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Today's rapid developments in digital media processing capabilities, and network speeds, make the dissemination of multimedia data extremely rapid and reliable, and have attracted significant research attention to video analysis, event detection, tracking and surveillance. In this work, a novel, generally applicable approach to the detection of human activity in video is presented. The areas of activity in the video are first detected via the accumulation and statistical processing of the motion vectors in all frames. The times (frames) at which events begin and end are defined as moments at which the statistical distribution of the motion vectors changes, for each pixel. These time instants are estimated in a novel manner, by applying sequential likelihood ratio testing on the motion vectors of the pixels that have been found to be active. The proposed system provides a theoretically sound solution for the detection of temporal changes in the human (or other) activity in video, without resorting to use of prior knowledge, heuristics, or ad-hoc thresholds. Sequential detection techniques allow us to find the frames where events begin and end, but also allows to pre-define the desired probabilities of false alarm and miss for the system. This is entirely novel for the temporal localization of activities and events in the video processing literature. Finally, sequential change detection methods require the smallest number of samples to detect a change, so they ensure the fastest detection of events. Experiments are performed with real sequences, involving human activities, for varying probabilities of false alarm and miss. Comparison with ground truth results shows that, indeed, the proposed method leads to meaningful localization of events both in time and in space.