.png){kind=icon}
Return
Multi-target tracking using CNN-based features: CNNMTT
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-018-6467-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Multi-target tracking
Machine vision
Tracking-by-detection
Multi-Object tracking
Video surveillance
Pedestrian tracking
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.0W
Citations:
3.2W
Organization
Cited Papers
ShareSaveShareSaveEfficiency of Sulfoaluminate Cement for Solidification of Simulated Radioactive Borate Liquid Waste
PREAI
ShareSaveShareSaveDamage in the Multiethnic Malaysian Systemic Lupus Erythematosus (SLE) Cohort: Comparison with Other Cohorts Worldwide.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAIShareSave
View more