Citation

Ullah FUM, Obaidat MS, Muhammad K, Ullah A, Baik SW, Cuzzolin F, Rodrigues JJPC, de Albuquerque VHC. Int. J. Intell. Syst. 2022; 37(12): 10400-10422.

Copyright

(Copyright © 2022, Hindawi / Wiley Periodicals)

DOI

10.1002/int.22537

PMID

unavailable

Abstract

Video surveillance has shown encouraging outcomes to monitor human activities and prevent crimes in real time. To this extent, violence detection (VD) has received substantial attention from the research community due to its vast applications, such as ensuring security over public areas and industrial settings through smart machine intelligence. However, because of changing illumination, complex background and low resolution, the analysis of violence patterns remains challenging in the industrial video surveillance domain. In this paper, we propose a computationally intelligent VD approach to precisely detect violent scenes through deep analysis of surveillance video sequential patterns. First, the video stream acquired through the vision sensor is processed by a lightweight convolutional neural network (CNN) for the segmentation of important shots. Next, temporal optical flow features are extracted from the informative shots via a residential optical flow CNN. These are concatenated with appearance-invariant features extracted from a Darknet CNN model. Finally, a multilayer long short-term memory network is plugged to generate the final feature map for learning the violence patterns in a sequence of frames. In addition, we contribute to the existing surveillance VD data set by considering its indoor and outdoor scenarios separately for the proposed method's evaluation, achieving a 2% increase in accuracy over surveillance fight data set. Experiments also show encouraging results over the state of the art on other challenging benchmark data sets.

Language: en

Keywords

artificial intelligence; complex pattern analysis; convolutional neural network; intelligent system; LSTM; violence detection