Title
Real-time resource allocation for tracking systems
Author
Satsangi, Y.
Whiteson, S.
Oliehoek, F.A.
Bouma, H.
Contributor
Elidan, G. (editor)
Kersting, K. (editor)
Publication year
2017
Abstract
Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting people, especially in ultra high resolution images. We propose a new algorithm called PartiMax that greatly reduces this cost by applying the person detector only to the relevant parts of the image. PartiMax exploits information in the particle filter to select k of the n candidate pixel boxes in the image. We prove that Parti- Max is guaranteed to make a near-optimal selection with error bounds that are independent of the problem size. Furthermore, empirical results on a real-life dataset show that our system runs in real-time by processing only 10% of the pixel boxes in the image while still retaining 80% of the original tracking performance achieved when processing all pixel boxes. Amazon Web Services; Artificial Intelligence Journal; Disney Research; et al.; Google Inc.; Microsoft Research
Subject
2015 Observation, Weapon & Protection Systems
II - Intelligent Imaging
TS - Technical Sciences
Artificial intelligence
Error analysis
Pixels
Tracking (position)
Automated tracking
Candidate pixels
Computational costs
Computer vision applications
Particle filter
Person detector
Tracking performance
Ultrahigh resolution
Real time systems
To reference this document use:
http://resolver.tudelft.nl/uuid:c4283faa-cdec-4888-a3c1-bbb1357707af
TNO identifier
781880
Publisher
AUAI Press Corvallis
Source
33rd Conference on Uncertainty in Artificial Intelligence, UAI 2017. 11 August 2017 through 15 August 2017
Document type
conference paper