Perimeter-intrusion event classification for on-line detection using multiple instance learning solving temporal ambiguities

J.A. Vijverberg, R.T.M. Janssen, R. Zwart, de, P.H.N. With, de

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

This paper describes a novel model for training an event detection system based on object tracking. We propose to model the training as a multiple instance learning problem, which allows us to train the classifier from annotated events despite temporal ambiguities. We apply this technique to realize a Perimeter Intrusion Detection (PID) algorithm and employ image-based features to distinguish real objects from moving vegetation and other distractions. An earlier developed tracking system is extended with the proposed technique to create an on-line PID-event detection system. Experiments with challenging videos show a reduction of the number of false positives by a factor 2–3 and improve the F1 detection performance from 0.15 to 0.28, when compared to a commercially available PID algorithm.
Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing, 27-30 October 2014, Paris, France
PublisherInstitute of Electrical and Electronics Engineers
Pages2408-2412
DOIs
Publication statusPublished - 2014

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