Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall

Bouma, Henri; Baan, Jan; Landsmeer, Sander H.; Kruszynski, Chris; Antwerpen, Gert van; Dijk, J. C. van

doi:10.1117/12.2016090

Cited by 20 publications

(22 citation statements)

References 23 publications

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“…Future work may include the improvement of threat recognition by combining track-based analysis with other features (e.g. action recognition for local motion [9][10] or re-identification information for long-term analysis [5] [7]) or by extending it to other forms of complex threatening behavior [2][26] [27]. Recently, we started a project in collaboration with the Royal Marechaussee and Qubit Visual Intelligence at Amsterdam's Schiphol Airport, where we intend to detect activities such as people falling on the ground and theft of bags amid large crowds.…”

Section: Discussionmentioning

confidence: 99%

“…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 and tracking was applied to the video data [5] resulting in 258.111 tracks. Actors entered the scene multiple times as pickpocket and victim.…”

Section: Annotation and Separation Of Train/test Datamentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Pedestrian Trackingmentioning

confidence: 99%

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

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“…For example, sterile-zone monitoring alerts operators for activities in industrial zones that should be abandoned at night. More advanced analysis is emerging to find suspects [7] and to detect suspicious behavior [9]. Besides fixed CCTV cameras we see an increasing number of sensors on mobile 'platforms', such as UAVs, vehicles, police officers and civilians.…”

Section: Relevance Of Vca For Bodycams In Security Applicationsmentioning

confidence: 99%

Video content analysis on body-worn cameras for retrospective investigation

et al. 2015

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In the security domain, cameras are important to assess critical situations. Apart from fixed surveillance cameras we observe an increasing number of sensors on mobile platforms, such as drones, vehicles and persons. Mobile cameras allow rapid and local deployment, enabling many novel applications and effects, such as the reduction of violence between police and citizens. However, the increased use of bodycams also creates potential challenges. For example: how can end-users extract information from the abundance of video, how can the information be presented, and how can an officer retrieve information efficiently? Nevertheless, such video gives the opportunity to stimulate the professionals' memory, and support complete and accurate reporting. In this paper, we show how video content analysis (VCA) can address these challenges and seize these opportunities. To this end, we focus on methods for creating a complete summary of the video, which allows quick retrieval of relevant fragments. The content analysis for summarization consists of several components, such as stabilization, scene selection, motion estimation, localization, pedestrian tracking and action recognition in the video from a bodycam. The different components and visual representations of summaries are presented for retrospective investigation.

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“…Hierdoor kan een operator sneller de verdachte terugvinden en gemakkelijker blijven volgen tot de veiligheidsdiensten ter plaatse zijn. 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

Automatische gedragsanalyse voor effectiever cameratoezicht in de openbare ruimte

Bouma¹,

Rest²,

Burghouts³

et al. 2014

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Real-time tracking and fast retrieval of persons in multiple surveillance cameras of a shopping mall

Cited by 20 publications

References 23 publications

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

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

Video content analysis on body-worn cameras for retrospective investigation

Automatische gedragsanalyse voor effectiever cameratoezicht in de openbare ruimte

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