Keyframe labeling technique for surveillance event classification

The huge amount of video data generated by surveillance systems necessitates the use of automatic tools for their efficient analysis, indexing, and retrieval. Automated access to the semantic content of surveillance videos to detect anomalous events is among the basic tasks; however, due to the high variability of the audio-visual features and large size of the video input, it still remains a challenging task, though a considerable amount of research dealing with automated access to video surveillance has appeared in the literature. We propose a keyframe labeling technique, especially for indoor environments, which assigns labels to keyframes extracted by a keyframe detection algorithm, and hence transforms the input video to an event-sequence representation. This representation is used to detect unusual behaviors, such as crossover, deposit, and pickup, with the help of three separate mechanisms based on finite state automata. The keyframes are detected based on a grid-based motion representation of the moving regions, called the motion appearance mask. It has been shown through performance experiments that the keyframe labeling algorithm significantly reduces the storage requirements and yields reasonable event detection and classification performance. © 2010 Society of Photo-Optical Instrumentation Engineers

Tools for Advanced Video Metadata Modeling

In this Thesis, we focus on problems in surveillance video analysis and propose advanced metadata modeling techniques to address them. First, we explore the problem of constructing a snapshot summary of people in a video sequence. We propose an algorithm based on the eigen-analysis of faces and present an evaluation of the method. Second, we present an algorithm to learn occlusion points in a scene using long observations of moving objects, provide an implementation and evaluate its performance. Third, to address the problem of availability and storage of surveillance videos, we propose a novel methodology to simulate video metadata. The technique is completely automated and can generate metadata for any scenario with minimal user interaction. Finally, a threat detection model using activity analysis and trajectory data of moving objects is proposed and implemented. The collection of tools presented in this Thesis provides a basis for higher level video analysis algorithms