Paper Info
Title
Multi-Perspective Vehicle Detection And Tracking: Challenges, Dataset, And Metrics
Abstract
The research community has shown significant improvements in both vision-based detection and tracking of vehicles, working towards a high level understanding of on-road maneuvers. Behaviors of surrounding vehicles in a highway environment is found as an interesting starting point, of why this dataset is introduced along with its challenges and evaluation metrics. A vision-based multi-perspective dataset is presented, containing a full panoramic view from a moving platform driving on U.S. highways capturing 2704x1440 resolution images at 12 frames per second. The dataset serves multiple purposes to be used as traditional detection and tracking, together with tracking of vehicles across perspectives. Each of the four perspectives have been annotated, resulting in more than 4000 bounding boxes in order to evaluate and compare novel methods.
Year
Venue
Keywords
2016
2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Vehicle detection, vehicle tracking, multi-perspective behavior analysis, autonomous driving
Field
DocType
Citations 
Computer vision,Simulation,Vehicle detection,Frame rate,Artificial intelligence,Engineering,Trajectory,Bounding overwatch,Highway environment
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jacob V Dueholm1112.69
Miklas S Kristoffersen2123.06
Ravi Kumar Satzoda37710.07
Eshed Ohn-Bar428223.09
Thomas Moeslund52721186.08
Mohan M. Trivedi66564475.50