Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking

Abstract

In recent decades, due to the groundbreaking improvements in machine vision, many daily tasks are performed by computers. One of these tasks is multiple-vehicle tracking, which is widely used in different areas such as video surveillance and traffic monitoring. This paper focuses on introducing an efficient novel approach with acceptable accuracy. This is achieved through an efficient appearance and motion model based on the features extracted from each object. For this purpose, two different approaches have been used to extract features, i.e. features extracted from a deep neural network, and traditional features. Then the results from these two approaches are compared with state-of-the-art trackers. The results are obtained by executing the methods on the UA-DETRACK benchmark. The first method led to 58.9% accuracy while the second method caused up to 15.9%. The proposed methods can still be improved by extracting more distinguishable features.

A Real-Time Multiple Object Tracking Network Under Complex Traffic Environment

“…Considering that the detector would fail to track objects because of occlusion and various poses, H. Sheng et al [9] constructed a heterogeneous association graph that fused high-level detection features and low-level image information for object association, and it performed well on the MOT17 datasets. Bafghi et al [10] used the appearance model and visual object tracking to achieve multiple vehicle tracking. The method led to 58.9% accuracy rate on the UA-DETRAC benchmark.…”