Real-time multiple object tracking using deep learning methods

• Published in: Neural computing & applications 2023-01, Vol.35 (1), p.89-118
• Main Authors: Meimetis, Dimitrios, Daramouskas, Ioannis, Perikos, Isidoros, Hatzilygeroudis, Ioannis
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer vision; Data Mining and Knowledge Discovery; Datasets; Dimensional analysis; Image Processing and Computer Vision; intelligence; Machine learning; Mathematical analysis; Multidimensional methods; Multiple target tracking; Object recognition; Pedestrians; Probability and Statistics in Computer Science; Real time; S.I.: information; Sorting algorithms; Special Issue on Information; systems and applications
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-021-06391-y

Multiple-object tracking is a fundamental computer vision task which is gaining increasing attention due to its academic and commercial potential. Multiple-object detection, recognition and tracking are quite desired in many domains and applications. However, accurate object tracking is very challenging, and things are even more challenging when multiple objects are involved. The main challenges that multiple-object tracking is facing include the similarity and the high density of detected objects, while also occlusions and viewpoint changes can occur as the objects move. In this article, we introduce a real-time multiple-object tracking framework that is based on a modified version of the Deep SORT algorithm. The modification concerns the process of the initialization of the objects, and its rationale is to consider an object as tracked if it is detected in a set of previous frames. The modified Deep SORT is coupled with YOLO detection methods, and a concrete and multi-dimensional analysis of the performance of the framework is performed in the context of real-time multiple tracking of vehicles and pedestrians in various traffic videos from datasets and various real-world footage. The results are quite interesting and highlight that our framework has very good performance and that the improvements on Deep SORT algorithm are functional. Lastly, we show improved detection and execution performance by custom training YOLO on the UA-DETRAC dataset and provide a new vehicle dataset consisting of 7 scenes, 11.025 frames and 25.193 bounding boxes.