Multiple-Vehicle Tracking in the Highway Using Appearance Model and Visual Object Tracking
Paper ID : 1073-MVIP2020
Fateme Bafghi *1, Bijan Shoushtarian2
2dept. Artificial Intelligence (of University of Isfahan)
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.
multiple-object tracking, feature extraction, deep learning, vehicle tracking
Status : Paper Accepted (Oral Presentation)