A Comprehensive Review of Deep Learning Methods for Detection and Tracking of Multiple Objects: A Review

Abstract

In recent years, the advancement of deep learning techniques has significantly transformed the fields of computer vision, particularly in object detection and tracking. This paper presents a comprehensive review of state-of-the-art deep learning methods utilized for the detection and tracking of multiple objects in various environments. We categorize the techniques based on their underlying architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid approaches, highlighting their strengths and limitations. The survey covers traditional algorithms as well as emerging deep learning models, examining their performance across different benchmarks and datasets. Furthermore, we address the challenges faced in real-time applications, such as occlusion, scale variation, and computational efficiency. By synthesizing current research trends, this review aims to provide insights for future developments in the domain, guiding researchers and practitioners toward effective solutions for multi-object detection and tracking tasks.