Multi-Object Detection and Tracking with Modified Optimization Classification in Video Sequences

Abstract

The paper presents a novel approach to enhancing multi-object detection and tracking in video sequences using a Modified Ant Swarm Optimization Deep Learning (ASO-DL) algorithm. The ASO-DL algorithm synergistically combines the optimization capabilities of ant swarm optimization with the powerful feature extraction abilities of deep learning models, resulting in a robust framework for realtime video analytics. Extensive simulations and experiments demonstrate significant improvements in key performance metrics, including accuracy, precision, recall, and F1 score, across various iterations. The proposed method consistently outperforms baseline models, achieving a final best fitness value of 0.96, with an accuracy of 0.98, precision of 0.99, and recall of 0.95. Additionally, classification results across different datasets such as CIFAR-10, IMDB, COCO, and ImageNet highlight the algorithm’s versatility and effectiveness. This research contributes to the field by providing a highly optimized solution for complex multi-object tracking tasks, offering substantial advancements in the accuracy and efficiency of real-time object detection systems. The findings hold significant potential for applications in surveillance, autonomous vehicles, and other domains requiring precise and reliable multi-object tracking.