Automatic surveillance in transportation hubs: No longer just about catching the bad guy

Denman, Simon; Kleinschmidt, Tristan; Ryan, David; Barnes, Paul; Sridharan, Sridha; Fookes, Clinton

doi:10.1016/j.eswa.2015.08.001

Cited by 15 publications

(12 citation statements)

References 127 publications

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“…In [32], the authors propose a system for monitoring queues by tracking people using a network of over-the-head video cameras. A solution to integrate existing monitoring technology is proposed in [1]. It studies methodologies for people counting and individual tracking for queue monitoring and behavior analysis.…”

Section: Previous Workmentioning

confidence: 99%

“…In this section, we show that using passenger counts [1], [2], is not enough to characterize the occupancy of the area being monitored.…”

Section: Queue Pattern Versus Throughputmentioning

confidence: 99%

“…1 displays the specific geometry of LIS X-ray area 1 , where passengers enter from the right, wait for their The authors are with the Institute for Systems and Robotics (ISR/IST), Instituto Superior Técnico, Universidade de Lisboa, Portugal. 1 The physical infrastructure described here no longer exists. turn and exit to the left towards individual X-ray booths.…”

Section: Introductionmentioning

confidence: 99%

“…In such a large space-more than 200m 2 -several configurations emerge, shaped by inflow variations, processing capacity, planning protocols and particular events. Currently, the global status of the X-ray system is monitored using global counts of in-out passenger flow [1], [2]. These global measures do not account for the internal state of the queue that reflects the instantaneous operating condition of the whole system.…”

Section: Introductionmentioning

confidence: 99%

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Understanding People Flow in Transportation Hubs

Carvalho

Marques

Costeira

2018

IEEE Trans. Intell. Transport. Syst.

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In this paper, we aim to monitor the flow of people in large public infrastructures. We propose an unsupervised methodology to cluster people flow patterns into the most typical and meaningful configurations. By processing 3D images from a network of depth cameras, we build a descriptor for the flow pattern. We define a data-irregularity measure that assesses how well each descriptor fits a data model. This allows us to rank flow patterns from highly distinctive (outliers) to very common ones. By discarding outliers, we obtain more reliable key configurations (classes). Synthetic experiments show that the proposed method is superior to standard clustering methods. We applied it in an operational scenario during 14 days in the X-ray screening area of an international airport. Results show that our methodology is able to successfully summarize the representative patterns for such a long observation period, providing relevant information for airport management. Beyond regular flows, our method identifies a set of rare events corresponding to uncommon activities (cleaning, special security and circulating staff).

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Section: Previous Workmentioning

confidence: 99%

“…In this section, we show that using passenger counts [1], [2], is not enough to characterize the occupancy of the area being monitored.…”

Section: Queue Pattern Versus Throughputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding People Flow in Transportation Hubs

Carvalho

Marques

Costeira

2018

IEEE Trans. Intell. Transport. Syst.

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“…Recent works [5] are just starting to develop vision algorithms with the aim of reaching the third generation of surveillance systems. Denman et al [6] argue that operational analytics, in the form of crowd counting and modelling [7], queue monitoring, or anomaly detection has received little attention in research. Even the commercial systems that do exist are few and work only with specially placed cameras.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Deep Learning Based Surveillance System for Open Critical Areas

et al. 2018

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How to automatically monitor wide critical open areas is a challenge to be addressed. Recent computer vision algorithms can be exploited to avoid the deployment of a large amount of expensive sensors. In this work, we propose our object tracking system which, combined with our recently developed anomaly detection system. can provide intelligence and protection for critical areas. In this work. we report two case studies: an international pier and a city parking lot. We acquire sequences to evaluate the effectiveness of the approach in challenging conditions. We report quantitative results for object counting, detection, parking analysis, and anomaly detection. Moreover, we report state-of-the-art results for statistical anomaly detection on a public dataset.

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Model of Surveillance System Based on Sound Tracking

Papež

Vlcek

2016

Automation Control Theory Perspectives in Intelligent Systems

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Automatic surveillance in transportation hubs: No longer just about catching the bad guy

Cited by 15 publications

References 127 publications

Understanding People Flow in Transportation Hubs

Understanding People Flow in Transportation Hubs

Deep Learning Based Surveillance System for Open Critical Areas

Model of Surveillance System Based on Sound Tracking

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