Title: A hybrid generative-discriminative model for abnormal event detection in surveillance video scenes

Authors: P.M. Ashok Kumar; D. Kavitha; S. Arun Kumar

Addresses: Department of Computer Science and Engineering, Laki Reddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, 521 530, India ' Department of Computer Science and Engineering, Laki Reddy Bali Reddy College of Engineering, Mylavaram, Andhra Pradesh, 521 530, India ' School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, 632 014, India

Abstract: Detecting anomalous events in densely pedestrian traffic video scenes remains challenging task, due to object's tracking difficulties and noise in the scene. In this paper, a Novel Hybrid Generative-Discriminative framework is proposed for detecting and localising the anomalous events of illegal vehicles present in the scene. This paper introduces a novelty in the application of Hybrid usage of latent Dirichlet allocation (LDA) and support vector machines (SVMs) over dynamic texture at sub-region level. The proposed HLDA-SVM model consists mainly of three steps: first local binary patterns from twelve orthogonal planes (LBP-TwP) technique is applied in each spatio-temporal video patch to extract dynamic texture; then LDA technique is applied to the extracted dynamic textures for finding the latent topic distribution and finally, training is done on the distribution of topic vector for each video sequence using multi way SVM classifier. The proposed HLDA-SVM model is validated on UCSD dataset data set and is compared with mixture of dynamic texture and motion context technique. Experimental results show that the HLDA-SVM approach performs well in par with current algorithms for anomaly detection.

Keywords: anomalous event detection; bag of visual words; dynamic textures; latent Dirichlet allocation; LDA; LBP-TwP; support vector machine; SVM.

DOI: 10.1504/IJICS.2020.105179

International Journal of Information and Computer Security, 2020 Vol.12 No.2/3, pp.253 - 268

Received: 27 Oct 2017
Accepted: 07 Sep 2018
Published online: 14 Feb 2020 *

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