Online target tracking via deep convolutional network approach

Abstract

There is a useful approach for multiple objects tracking easy and efficient that is called simple online and real time tracking(SORT). SORT algorithm performance can be improved by adding visual information. This can reduce the number of identity switches. Because the main framework of the algorithm has a lot of computational complexity, a deep network has been used that is offline on a large data set of trained pedestrians. the focus of this article is on the architecture of this deep network in order to extract more and higher quality visual information that can help the object recognition algorithm. The paper also used a particle filter instead of a Kalman filter to improve data association performance. We tested our proposed method on two standard datasets, MOT16 and MOT17, and compared its performance with other available methods. The results show that the tracking accuracy(52.2) on the MOT17 dataset is improved compared to the existing methods in this field. Experimental evaluation shows that our proposed architecture improves the number of identity switches and ideally tracks goals in complex environments.