Multi-camera tracking using a Multi-Goal Social Force Model

Mazzon, Riccardo; Cavallaro, Andrea

doi:10.1016/j.neucom.2011.09.038

Cited by 43 publications

(14 citation statements)

References 14 publications

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“…The optimization in the sensor network is necessary by reducing the sensors density and promoting effective monitoring of the entire of the trade centre. The one measurement point including sensors and camera [15], [16] to collect temperature, humidity, light, dust, and visual images. The number and density of measurement points depend on the geographical layout of the trade centre.…”

Section: Function and Operationmentioning

confidence: 99%

Design of public safety network and emergency alarm for smart trade centre

Le¹,

Nguyen²,

Nguyen³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

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In the trade centre, there are always unannounced emergencies that need to be addressed to maintain order. Therefore, a smart system is required to detect threats and deal with them. In this paper, we propose a secure network system that is structured into three layers including Unmanned Aerial Vehicle (UAV) devices, self-acting robots, and sensor networks. The system operates in two directions: 1) The sensor network tracks the environment and provides raw data to the robots, from here will be transmitted directly to the storage and processing centre or the nearest UAV devices and 2) the administrator or operation centre gives operational instructions to the UAV devices. Based on the analysis of the environmental data on the cloud server, the proposed system can automatically generate warnings and actions in time.

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Section: Function and Operationmentioning

confidence: 99%

Design of public safety network and emergency alarm for smart trade centre

Le¹,

Nguyen²,

Nguyen³

et al. 2018

EAI Endorsed Transactions on Industrial Networks and Intelligen

View full text Add to dashboard Cite

show abstract

“…In [28] a track-before-detect particle filter (TBD-PF) is used to increase track consistency against noisy data for multi-camera multi-target fusion and tracking. In [29] a modified Social Force Model (SFM) with a goaldriven approach for multi-camera tracking is proposed. This work takes into account key regions as potential intersections where people can change the direction of motion.…”

Section: A Related Workmentioning

confidence: 99%

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Chen

Bhanu

2015

IEEE Sensors J.

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Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments.

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“…On the other hand, [10] makes use of semantic information to identify groups from independent trackers. [18] introduces a multi-camera tracking system with non-overlapping field of view. It uses a social force model to generate multiple hypothesis about the movements of a non-observed target who has left the field of view of a camera.…”

Section: Related Workmentioning

confidence: 99%

Improving multiple pedestrians tracking with semantic information

Madrigal

Hayet

Lerasle

2014

SIViP

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This work presents an interacting multiple pedestrian tracking method for monocular systems that incorporates a prior knowledge about the environment and about interactions between targets. Pedestrian motion being ruled by both environment and social aspects, we model these complex behaviors by considering 4 cases of motion: going straight; finding one's way; walking around and standing still. They are combined within an Interacting Multiple Model Particle Filter strategy. We model targets interactions with social forces, included as potential functions in the weighting process of the Particle Filter. We use different social force setups within each motion model to handle high level behaviors (collision avoidance, flocking. . . ). We evaluate our algorithm on challenging datasets and show that such semantic information improves the tracker performance compared to the literature.

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Multi-camera tracking using a Multi-Goal Social Force Model

Cited by 43 publications

References 14 publications

Design of public safety network and emergency alarm for smart trade centre

Design of public safety network and emergency alarm for smart trade centre

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Improving multiple pedestrians tracking with semantic information

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