A Human Abnormal BehaviorRecognition and Collaborative Tracking Algorithm

Xiong, Jing; Liu, Zhijing; Xue, Huaiguo; Tang, Guoliang

doi:10.14257/ijsip.2015.8.12.11

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Feature Matching and Collaborative Tracking1

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2016

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“…BRM are responsible for recognition the behavior of moving target in some important scene using the algorithm that combine action library matching with moving trajectory detection [10]. The part is not the key point of the paper, but feature extraction would be process when BRM detect a suspicious behavior so as to CTM performance collaborative tracking.…”

Section: Feature Matching and Collaborative Trackingmentioning

confidence: 99%

A Parallel Computing Algorithm for Moving Targets Tracking in Wireless Sensor Networks

Xiong¹,

Liu²,

Tang³

2016

Proceedings of the 2016 International Forum on Management, Education and Information Technology Application

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Abstract. In this paper, we address the problem of moving target feature extraction and collaborative tracking in wireless sensor networks (WSN), and present a parallel computing algorithm for moving human collaborative tracking. At first, WSN optimization deployment: divide the monitors in WSN into the two types: Behavior Recognition Monitor (BRM) and Collaborative Tracking Monitor (CTM), and settle all the monitors utilizing FCM algorithm into many groups. Secondly, parallel detection and behavior recognition to get the collaborative tracking target. Finally, a multi-points feature extraction scheme for WSN monitors to track the suspicious target collaboratively. We also compare our algorithm with three existing solutions, the statistics result shows that our scheme has a better detection accuracy and tracking performance.

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Section: Feature Matching and Collaborative Trackingmentioning

confidence: 99%