Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association

Authors:
Qian Yu;Gerard Medioni
Affiliations:
University of Southern California, Los Angeles;University of Southern California, Los Angeles
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

Citing 0
Cited 8

Multi-target tracking with occlusions via skeleton points assignment

Neurocomputing
Multiple target tracking using cognitive data association of spatiotemporal prediction and visual similarity

Pattern Recognition
Visual tracking of numerous targets via multi-Bernoulli filtering of image data

Pattern Recognition
Robust hierarchical multiple hypothesis tracker for multiple-object tracking

Expert Systems with Applications: An International Journal
Extended MHT algorithm for multiple object tracking

Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Graph mining for object tracking in videos

ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Carried object detection and tracking using geometric shape models and spatio-temporal consistency

ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Navigation algorithm for WSN mobile node on MH particle filtering improvement

International Journal of Sensor Networks

Quantified Score

Hi-index	0.15

Visualization

Abstract

We propose a framework for tracking multiple targets, where the input is a set of candidate regions in each frame, as obtained from a state-of-the-art background segmentation module, and the goal is to recover trajectories of targets over time. Due to occlusions by targets and static objects, as also by noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore, the one-to-one assumption used in most data association algorithms is not always satisfied. Our method overcomes the one-to-one assumption by formulating the visual tracking problem in terms of finding the best spatial and temporal association of observations, which maximizes the consistency of both motion and appearance of trajectories. To avoid enumerating all possible solutions, we take a Data-Driven Markov Chain Monte Carlo (DD-MCMC) approach to sample the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint probability model combining motion and appearance. Comparative experiments with quantitative evaluations are provided.