Publication Type:

Conference Paper

Source:

2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, Institute of Electrical and Electronics Engineers Inc. (2017)

ISBN:

9781509045594

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030239879&doi=10.1109%2fICACCS.2017.8014719&partnerID=40&md5=e08ce3ed797b81b3b763324d5fc33ffc

Keywords:

Foreground objects, Image segmentation, Intelligent K-Means, Motion Vectors, Multiobjective optimization, Object Detection, Object recognition, Occlusion, Optimization, Security systems, Stereo image processing, Technical solutions, Thresholding, Video analytics, Video surveillance

Abstract:

The paper proposes an intelligent K-means segmentation algorithm that clearly segments foreground objects and completely occluded objects. When a person completely occludes an object while entering into the area of video surveillance, it is considered as an anomaly. The paper comes up with a robust technical solution to address this. The proposed algorithm chooses an optimal value for K and segments the object. The scope of the system extends to the area such as prison, airport etc. where there is a need to monitor completely occluded objects and other objects in the foreground. The system is tested with images from Stereo Thermal Dataset and achieves a precision rate of 88.89% while segmenting objects. From the experimental results, we infer that the proposed algorithm is robust in segmenting the objects without losing its shape and number. © 2017 IEEE.

Notes:

cited By 0; Conference of 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017 ; Conference Date: 6 January 2017 Through 7 January 2017; Conference Code:130103

Cite this Research Publication

K. S. Gautam and Thangavel, S. K., “Hidden object detection for classification of threat”, in 2017 4th International Conference on Advanced Computing and Communication Systems, ICACCS 2017, 2017.

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