Multiple object tracking in sensor networks using distributed clique finding

Javed, Nauman; Wolf, Tilman

doi:10.1109/iccnc.2013.6504253

Cited by 3 publications

(1 citation statement)

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“…FaceTrack [34] is a target tracking framework which is used to construct faces, using the nodes of a spatial region surrounding a target. A target tracking algorithm based on clique, for binary proximity sensor networks, is presented by Javed et al [35]. This work formed cliques consisting of sensors and achieved less location error in TT.…”

Section: Related Workmentioning

confidence: 99%

Detecting Anonymous Target and Predicting Target Trajectories in Wireless Sensor Networks

Rani¹,

Kumar²

2021

Symmetry

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Target Tracking (TT) is an application of Wireless Sensor Networks (WSNs) which necessitates constant assessment of the location of a target. Any change in position of a target and the distance from each intermediate sensor node to the target is passed on to base station and these factors play a crucial role in further processing. The drawback of WSN is that it is prone to numerous constraints like low power, faulty sensors, environmental noises, etc. The target should be detected first and its path should be tracked continuously as it moves around the sensing region. This problem of detecting and tracking a target should be conducted with maximum accuracy and minimum energy consumption in each sensor node. In this paper, we propose a Target Detection and Target Tracking (TDTT) model for continuously tracking the target. This model uses prelocalization-based Kalman Filter (KF) for target detection and clique-based estimation for tracking the target trajectories. We evaluated our model by calculating the probability of detecting a target based on distance, then estimating the trajectory. We analyzed the maximum error in position estimation based on density and sensing radius of the sensors. The results were found to be encouraging. The proposed KF-based target detection and clique-based target tracking reduce overall expenditure of energy, thereby increasing network lifetime. This approach is also compared with Dynamic Object Tracking (DOT) and face-based tracking approach. The experimental results prove that employing TDTT improves energy efficiency and extends the lifetime of the network, without compromising the accuracy of tracking.

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Section: Related Workmentioning

confidence: 99%