Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall
conference paper
Proactive 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.
TNO Identifier
511413
Publisher
SPIE
Source title
Optics and Photonics for Counterterrorism, Crime Fighting, and Defence X; and Optical Materials and Biomaterials in Security and Defence Systems Technology XI, 22 September 2014, Amsterdam, The Netherlands
Editor(s)
Burgess, D.
et al
et al
Place of publication
Bellingham,WA