Enhancing Anomaly Detection in Pedestrian Walkways using Improved Sparrow Search Algorithm with Parallel Features Fusion Model 

Y. Sreeraman¹, D. Jagadeesan^1,*, J. Jegan¹, T. Vivekanandan¹, A. Srinivasan², G. Asha³

¹Department of Computer Science and Engineering, School of Technology, The Apollo University, Chittoor, Andhra Pradesh, India; 

²Department of Computer Science and Engineering (Data Science), Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, India;

³Department of Electronics and Communication Engineering, Adhiparasakthi College of Engineering, Kalavai, Ranipet District, Tamil Nadu, India;

Emails: sramany@gmail.com; djagadeesanphd@gmail.com; jegan.deepa@gmail.com; stvanand@gmail.com; srini.vit@gmail.com; ashajagadeesan@gmail.com

^*Corresponding Author: djagadeesanphd@gmail.com

Abstract

Anomaly detection in pedestrian walkways is a vital research area, widely employed to enhance the safety of the pedestrians. Because of the widespread usage of the video surveillance systems and the increasing number of captured videos, the conventional manual examination of labeling abnormal events is a laborious process. Therefore, an automatic surveillance system to accurately detect anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. This article develops an Improved Meta-heuristic with Parallel Features Fusion Model for Anomaly Detection in Pedestrian Walkways (IMPFF-ADPW) method. The main aim of the IMPFF-ADPW approach is to recognize the existence of anomalies in pedestrian walkways. To obtain this, the IMPFF-ADPW method applies a joint bilateral filter (JBF) for the process of noise removal. Besides, a parallel fusion process comprising NasNet Mobile and Darknet-53 models can be utilized for feature extraction. For the anomaly detection method, the deep autoencoder (DAE) model is applied and its hyperparameters are finetuned by using an improved sparrow search algorithm (ISSA). A wide of experimental outcomes can be applied to the UCSD database to illustrate the betterment of the IMPFF-ADPW methodology. The simulation values indicated the enhanced performance of the IMPFF-ADPW method over other existing techniques.

Keywords: Computer vision; Pedestrian walkways; Anomaly detection; Metaheuristics; Deep learning