Title
(MP)2T: multiple people multiple parts tracker
Abstract
We present a method for multi-target tracking that exploits the persistence in detection of object parts. While the implicit representation and detection of body parts have recently been leveraged for improved human detection, ours is the first method that attempts to temporally constrain the location of human body parts with the express purpose of improving pedestrian tracking. We pose the problem of simultaneous tracking of multiple targets and their parts in a network flow optimization framework and show that parts of this network need to be optimized separately and iteratively, due to inter-dependencies of node and edge costs. Given potential detections of humans and their parts separately, an initial set of pedestrian tracklets is first obtained, followed by explicit tracking of human parts as constrained by initial human tracking. A merging step is then performed whereby we attempt to include part-only detections for which the entire human is not observable. This step employs a selective appearance model, which allows us to skip occluded parts in description of positive training samples. The result is high confidence, robust trajectories of pedestrians as well as their parts, which essentially constrain each other's locations and associations, thus improving human tracking and parts detection. We test our algorithm on multiple real datasets and show that the proposed algorithm is an improvement over the state-of-the-art.
Year
DOI
Venue
2012
10.1007/978-3-642-33783-3_8
ECCV (6)
Keywords
Field
DocType
multiple parts tracker,human part,multiple people,human tracking,parts detection,improved human detection,multi-target tracking,explicit tracking,entire human,human body part,pedestrian tracking,initial human tracking,human body
Flow network,Computer vision,Observable,Computer science,Tracking system,Exploit,Active appearance model,Artificial intelligence,Merge (version control)
Conference
Citations 
PageRank 
References 
10
0.54
20
Authors
4
Name
Order
Citations
PageRank
Hamid Izadinia116411.16
Imran Saleemi242515.76
Wenhui Li3100.54
Mubarak Shah416522943.74