NDLI: Multiframe Many–Many Point Correspondence for Vehicle Tracking in High Density Wide Area Aerial Videos

Content Provider	SpringerLink
Author	Saleemi, Imran Shah, Mubarak
Copyright Year	2013
Abstract	This paper presents a novel framework for tracking thousands of vehicles in high resolution, low frame rate, multiple camera aerial videos. The proposed algorithm avoids the pitfalls of global minimization of data association costs and instead maintains multiple object-centric associations for each track. Representation of object state in terms of many to many data associations per track is proposed and multiple novel constraints are introduced to make the association problem tractable while allowing sharing of detections among tracks. Weighted hypothetical measurements are introduced to better handle occlusions, mis-detections and split or merged detections. A two-frame differencing method is presented which performs simultaneous moving object detection in both. Two novel contextual constraints of vehicle following model, and discouragement of track intersection and merging are also proposed. Extensive experiments on challenging, ground truthed data sets are performed to show the feasibility and superiority of the proposed approach. Results of quantitative comparison with existing approaches are presented, and the efficacy of newly introduced constraints is experimentally established. The proposed algorithm performs better and faster than global, 1–1 data association methods.
Starting Page	198
Ending Page	219
Page Count	22
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	104
Issue Number	2
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2013-04-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Wide area aerial surveillance Multi target tracking Multiframe data association Multiple hypothesis Multiple candidate tracking CLIF dataset Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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