期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 78, 期 6, 页码 7077-7096出版社
SPRINGER
DOI: 10.1007/s11042-018-6467-6
关键词
Multi-target tracking; Machine vision; Tracking-by-detection; Multi-Object tracking; Video surveillance; Pedestrian tracking
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.
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