aspg-31-4406-1791687023 20261011025023 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2026 8 1 V. Vinothini Assistant Professor, Syed Ammal Engineering College, Tamil Nadu, India N. Devi Assistant Professor, Syed Ammal Engineering College, Tamil Nadu, India R. Roja Assistant Professor, Syed Ammal Engineering College, Tamil Nadu, India G. Mahendran Professor, Syed Ammal Engineering College, Tamil Nadu, India Multiple moving object detection and tracking are challenging roles in many computer vision applications such as object navigation and human identification. Object tracking is one of the key challenges for securing against crime, supporting public safety, and enabling effective traffic management systems. In video surveillance applications, detection of multiple moving vehicles from video is the major task for tracking and understanding the behavior of the detected objects. Performance of object detection algorithms is degraded by factors such as fog or haze, occlusion, dynamic background, poor illumination, and low resolution. Fog is one of the major bottlenecks of video surveillance applications. The proposed Dark Channel Prior algorithm using guided filter (GDCP) is adapted for fog removal. The Gaussian Mixture Model (GMM) is proposed for detecting multiple moving objects, and features are extracted from the detected objects using Motion Vector Estimation. The K-Nearest Neighbor algorithm is used for tracking the moving objects (vehicles) using the detected features. Efficiency is improved due to the adoption of the proposed fog-removal algorithm and feature extraction for effective tracking. There are wide varieties of applications in moving object detection and tracking. 2026 01 04 4406 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.080101 https://www.americaspg.com/journal/31/article/4406 https://www.americaspg.com/storage/articles/manuscripts/01M333AKXF17D1EB4P57QK9J3P.pdf