Finding suspects in multiple cameras for improved railway protection

Marck, Jan Willem; Bouma, Henri; Baan, Jan; Filho, Julio de Oliveira; Brink, M.

doi:10.1117/12.2064020

Cited by 3 publications

(2 citation statements)

References 30 publications

(19 reference statements)

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“…We have a system that generates detections and tracks in each camera [5] [23]. These tracks can be used for multi-camera tracking and re-identification [1][19] [6][27] and further behavior analysis [4].…”

Section: Pedestrian Trackingmentioning

confidence: 99%

Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

et al. 2014

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Rest, "Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall," Proc. SPIE, Vol. 9253, (2014). http://dx.doi. org/10.1117/12.2066851 Copyright 2014 Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only.Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. Automatic detection of suspicious behavior of pickpockets with trackbased features in a shopping mall ABSTRACTProactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature computation and pickpocket recognition. This is challenging because the environment is crowded, people move freely through areas which cannot be covered by a single camera, because the actual snatch is a subtle action, and because collaboration is complex social behavior. We carried out an experiment with more than 20 validated pickpocket incidents. We used a top-down approach to translate expert knowledge in features and rules, and a bottom-up approach to learn discriminating patterns with a classifier. The classifier was used to separate the pickpockets from normal passers-by who are shopping in the mall. We performed a cross validation to train and evaluate our system. In this paper, we describe our method, identify the most valuable features, and analyze the results that were obtained in the experiment. We estimate the quality of these features and the performance of automatic detection of (collaborating) pickpockets. The results show that many of the pickpockets can be detected at a low false alarm rate.

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Section: Pedestrian Trackingmentioning

confidence: 99%

Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

et al. 2014

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“…Uit een human-in-theloop operator experiment in een druk winkelcentrum is gebleken dat een dergelijk systeem leidt tot 37% minder gemiste personen, wat een significante verbetering is (Bouma, Baan et al, 2013). De architectuur blijkt volwassen genoeg te zijn om live demonstraties mee uit te voeren, zoals bijvoorbeeld in Polen in het EU-FP7 project PROTECTRAIL (Marck et al, 2014), waarbij een persoon is teruggevonden na een (in scene gezet) incident met achtergelaten bagage.…”

Section: Automatisch Volgen Van Een Overvaller Na Een Overvalunclassified