Approaches to Video Real time Multi-Object Tracking and Object Detection: A survey

Abstract

The world is living a major shift from information era to artificial intelligence (AI) era. Machines are giving the ability to sense the surrounding world and to take decisions. Computer vision and especially multi-object tracking(MOT), which relies on Deep Learning, is at the heart of this shift. Indeed, with the growth of deep learning, the methods and algorithms that are tackling this problem have gained better performance from the integration of deep learning models. Deep Learning has been demonstrated as MOT, which tackles the challenges of in-and-out objects, unlabeled data, confusing appearance and occlusion. Deep learning, which relied on MOT techniques, has recently gained a fast ground from representation learning to modelling the networks thanks to the advancement of deep learning hypothesis and benchmark arrangement. This paper sums up and analyzes deep learning based MOT techniques which are at a highest level. The paper also offers a comprehensive review about the different techniques applied in MOT of deep learning based on different methods. Furthermore, this study analyzes the benefits and the constraints of current strategies, techniques and methods.