Asset Details
Do you wish to reserve the book?
Multi-target tracking using CNN-based features: CNNMTT
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Multi-target tracking using CNN-based features: CNNMTT
Do you wish to request the book?
Multi-target tracking using CNN-based features: CNNMTT
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Multi-target tracking using CNN-based features: CNNMTT
2019
Overview
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.
Publisher
Springer Nature B.V
Subject
