A review on deep learning-based object tracking methods

Abstract

A deep learning algorithm tracks an object’s movement during object tracking and the main challenge in the tracking of objects is to estimate or forecast the locations and other pertinent details of moving objects in a video. Typically, object tracking entails the process of object detection. In computer vision applications the detection, classification, and tracking of objects play a vital role, and gaining information about the various techniques available also provides significance. In this research, a systematic literature review of the object detection techniques is performed by analyzing, summarizing, and examining the existing works available. Various state of art works are collected from standard journals and the methods available, cons, and pros along with challenges are determined based on this the research questions are also formulated. Overall, around 50 research articles are collected, and the evaluation based on various metrics shows that most of the literary works used Deep convolutional neural networks (Deep CNN), and while tracking the objects object detection helps in enhancing the performance of these networks. The important issues that need to be resolved are also discussed in this research, which helps in leveling up the object-tracking techniques.