Research Article
Multi-Object Detection and Tracking with Modified Optimization
Classification in Video Sequences
S. Prabu1,*, A.B. Hajira Be2 and Syed Raffi Ahamed J3
1Assistant Professor, Department of Computing Technologies, School of Computing, SRM Institute of
Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India.
2Associate Professor, Department of Computer Applications, Karpaga Vinayaga College of Engineering
and Technology, Maduranthagam Taluk, Tamil Nadu, 603308, India.
3Assistant Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering
College, Thandalam, Mevalurkuppam, Tamil Nadu 602105, India.
*Corresponding Author Name: S. Prabu. Email: drprabucse@gmail.com
Journal of Computer Allied Intelligence(JCAI),30 June
2024,2(3),15-27
Received: 05 May 2024 Accepted: 20 June 2024 Published: 30 June 2024
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.
Keywords: Multi-Object Detection; Optimization; Classification; Tracking; Real-time Objects.
Citation : S. Prabu, A.B. Hajira Be and Syed Raffi Ahamed J “Multi-Object Detection and Tracking with Modified Optimization
Classification in Video Sequences”, Journal of Computer Allied Intelligence (JCAI), vol.02, no.03, pp.15-27,2024.
