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Trajectory-Based Anomalous Event Detection
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BibTeX
@MISC{Piciarelli_trajectory-basedanomalous,
author = {Claudio Piciarelli and Christian Micheloni and Gian Luca Foresti and Senior Member},
title = {Trajectory-Based Anomalous Event Detection},
year = {}
}
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Abstract
Abstract—During the last years, the task of automatic event analysis in video sequences has gained an increasing attention among the research community. The application domains are disparate, ranging from video surveillance to automatic video annotation for sport videos or TV shots. Whatever the application field, most of the works in event analysis are based on two main approaches: the former based on explicit event recognition, focused on finding highlevel, semantic interpretations of video sequences, and the latter based on anomaly detection. This paper deals with the second approach, where the final goal is not the explicit labeling of recognized events, but the detection of anomalous events differing from typical patterns. In particular, the proposed work addresses anomaly detection by means of trajectory analysis, an approach with several application fields, most notably video surveillance and traffic monitoring. The proposed approach is based on single-class support vector machine (SVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous trajectories. Particular attention is given to trajectory classification in absence of a priori information on the distribution of outliers. Experimental results prove the validity of the proposed approach. Index Terms—Anomaly detection, event analysis, support vector machines (SVMs), trajectory clustering.
Keyphrases
trajectory-based anomalous event detection event analysis video sequence anomaly detection video surveillance second approach automatic event analysis trajectory clustering anomalous event semantic interpretation index term anomaly detection automatic video annotation last year priori information several application field traffic monitoring main approach tv shot sport video single-class support vector machine anomalous trajectory novelty detection svm capability application domain application field final goal particular attention trajectory classification explicit event recognition support vector machine recognized event research community trajectory analysis typical pattern experimental result explicit labeling
