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Abnormal event detection in videos using binary features
conference contribution
posted on 2017-01-01, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun Li© 2017 IEEE. In this paper we address the problem of online video abnormal event detection. A vast number of methods to automatically detect abnormal events in videos have been recently proposed. However, the majority of these recently proposed methods cannot attain online performance; in other words, they cannot detect events as soon as they occur. Thus there is a lack of methods specifically aimed to detect events in online fashion. In this paper, we propose to incorporate binary features to detect abnormal events in an online manner. This is based on the fact that binary features are well known to require short processing times, compared to double-precision features. The main contribution of this work is then at the feature extraction step. Our experiment results of our binary-based framework show that our proposed binary features help to reduce processing times for anomaly detection, while outperforming other online methods, in terms of detection accuracy.
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Event
Telecommunications and Signal Processing. Conference (2017 : Barcelona, Spain)Pagination
621 - 625Publisher
IEEELocation
Barcelona, SpainPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2017-07-05End date
2017-07-07ISBN-13
9781509039821Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
TSP 2017 : Proceedings of the 40th International Conference on Telecommunications and Signal ProcessingUsage metrics
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