Open Access

ARTICLE

Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

G. Rajasekaran^1,*, J. Raja Sekar²

1 Department of Information Technology, Mepco Schlenk Engineering College, Sivakasi, 626005, India
2 Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, 626005, India

* Corresponding Author: G. Rajasekaran. Email:

Intelligent Automation & Soft Computing 2023, 35(2), 2399-2412. https://doi.org/10.32604/iasc.2023.029119

Received 25 February 2022; Accepted 21 April 2022; Issue published 19 July 2022

Abstract

Abnormal behavior detection is challenging and one of the growing research areas in computer vision. The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events. In this work, Pyramidal Lucas Kanade algorithm is optimized using EMEHOs to achieve the objective. First stage, OPLKT-EMEHOs algorithm is used to generate the optical flow from MIIs. Second stage, the MIIs optical flow is applied as input to 3 layer CNN for detect the abnormal crowd behavior. University of Minnesota (UMN) dataset is used to evaluate the proposed system. The experimental result shows that the proposed method provides better classification accuracy by comparing with the existing methods. Proposed method provides 95.78% of precision, 90.67% of recall, 93.09% of f-measure and accuracy with 91.67%.

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Keywords

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