Contextual person detection in multi-modal outdoor surveillance

Neil Robertson, Jonathan Letham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
53 Downloads (Pure)


In this paper we present a new approach to person detection
in outdoor surveillance tasks. A multi-modal segmentation
(RGB, Polarimetric, thermal sensors) of the world into regions
sky, road, bush, trees, grass etc. is used to learn the
normal spatial context of people appearing in normal training
data. The context feature is a novel application of the work of
Wolf et al. [1] which enables the probability of a person appearing
in a certain location to be computed. By using motion
as a precursor to the deployment of a HOG person detector
in conjunction with the spatial context likelihood we obtain
significant improvement in person detection for challenging
scenes. Comprehensive ROC analysis on 4 outdoor scenes is
reported for normal activity detection. Anomaly detection is
then achieved using learned context and we show that 72% of
true positive anomalies are found for a false positive rate of
0.19% over all data in thermal and visual band data.
Original languageEnglish
Title of host publication2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 27-31 Aug 2012
Publication statusPublished - 27 Aug 2012


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