Jan Prokaj, Ph.D.
Founder
- Bibliography sites:
- Google Scholar
- DBLP
- MS Academic
- Personal links:
- YouTube
Inferring Tracklets for Multi-Object Tracking
Problem
- Recent algorithms solve the tracking problem hierarchically: first find short tracks (tracklets), then iteratively extend into longer tracks
- How to optimally determine tracklets?
Approach
- Consider object detections in a sliding window of frames (4-8 seconds)
- Build an association graph (DAG), or detection graph (representing all possible associations over a sliding window), for each detection in the first frame of the sliding window
- Label detections in this graph as valid (consistent with the initial detection) or invalid (inconsistent)
- MAP inference in a Bayesian network
- Generate track(s) from the resulting sequence of valid detections
Results
Data
- freeway01
Images: freeway01.zip (76 frames, 1207x1229, 26.6 MiB)
- seq21
Images: seq21.zip (63 frames, 695x995, 30.9 MiB)
Tracker configuration: seq21_config.xml
Metadata: seq21_meta.xml
- seq30
Images: seq30.zip (101 frames, 1312x738, 66.7 MiB)
Code
Reference
J. Prokaj , M. Duchaineau, G. Medioni. Inferring Tracklets for Multi-Object Tracking. In Workshop of Aerial Video Processing joint with IEEE CVPR , pages 37-44, 2011.
Related Publications
J. Prokaj . Exploitation of Wide Area Motion Imagery . Ph.D. thesis, University of Southern California, 2013.
J. Prokaj , X. Zhao, G. Medioni. Tracking Many Vehicles in Wide Area Aerial Surveillance. In Workshop on Camera Networks and Wide Area Scene Analysis joint with IEEE CVPR , pages 37-43, 2012.
J. Prokaj , M. Duchaineau. Detection and Tracking of Vehicles in Aerial Imagery . Lawrence Livermore National Lab internship, 2009.