Multi-target tracking using CNN-based features: CNNMTT

• Published in: Multimedia tools and applications Vol. 78; no. 6; pp. 7077 - 7096
• Main Authors: Mahmoudi, Nima, Ahadi, Seyed Mohammad, Rahmati, Mohammad
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2019; Springer Nature B.V
• Subjects: Algorithms; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Multimedia Information Systems; Multiple target tracking; Special Purpose and Application-Based Systems; Tracking-by-detection; Video surveillance; Multi-target tracking; Machine vision; Pedestrian tracking; Multi-Object tracking
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-6467-6

In this paper, we focus mainly on designing a Multi-Target Object Tracking algorithm that would produce high-quality trajectories while maintaining low computational costs. Using online association, such features enable this algorithm to be used in applications like autonomous driving and autonomous surveillance. We propose CNN-based, instead of hand-crafted, features to lead to higher accuracies. We also present a novel grouping method for 2-D online environments without prior knowledge of camera parameters and an affinity measure based on the groups maintained in previous frames. Comprehensive evaluations of our algorithm (CNNMTT) on a publicly available and widely used dataset (MOT16) reveal that the CNNMTT method achieves high quality tracking results in comparison to the state of the art while being faster and involving much less computational cost.