Object tracking using optimized Dual interactive Wasserstein generative adversarial network from surveillance video

• Published in: Knowledge-based systems Vol. 311; p. 113084
• Main Author: Srinivasan, Karthik
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 28.02.2025
• Subjects: Anisotropic diffusion Kuwahara filtering; Dual interactive Wasserstein generative adversarial network; Residual exemplars local binary pattern; Dual interactive Wasserstein generative adversarial network; Residual exemplars local binary pattern; Anisotropic diffusion Kuwahara filtering
• ISSN: 0950-7051
• DOI: 10.1016/j.knosys.2025.113084

Object tracking in videos is crucial for applications such as video analytics, video surveillance, and intelligent transportation systems. Despite important advancements, challenges like occlusions, background noise, variable object counts, and object appearance similarity still hinder effective tracking. To overcome these complications, Object Tracking using Optimized Dual Interactive Wasserstein Generative Adversarial Network from Surveillance Video (OTSV-DWGAN-GPCOA) is proposed. The input data is collected from the Moving Objects Video Clips Dataset. During the Pre-Processing Phase, noise removal and background subtraction are performed using Anisotropic Diffusion Kuwahara Filtering (ADKF), transforming the surveillance video clips into unique frames for analysis. In the Moving Object Detection Phase, Residual Exemplars Local Binary Pattern (RELBP) is utilized to extract morphological features such as size, texture, color, intensity, shape, and contrast. Additionally, Adaptive Density-Based Spatial Clustering (ADSC) is employed to detect moving objects. In the Moving Object Tracking Phase, the Giza Pyramids Construction Optimization Algorithm (GPCOA) optimizes the parameters of the DWGAN to improve tracking accuracy. Once objects are successfully tracked, the output represents the tracking steps. In the final phase, Moving Object Prediction utilizes the Minkowski Distance Metric to predict the position of tracked objects in each frame. The OTSV-DWGAN-GPCOA method is implemented in Python and assessed using performance metrics. The method achieves 20.11 %, 24.16 % and 22.23 % higher accuracy, 22.45 %, 19.34 % and 24.22 % higher Tracking rate analyzed with existing techniques such asMOD-YOLOv2-SV,ODL-CNN-MRCED, and AR-SSN-VSSC respectively.