Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Online multi-object tracking with a single moving camera is a challenging problem as the assumptions of 2D conventional motion models (e.g., first or second order models) in the image coordinate no longer hold because of global camera motion. In this paper, we consider motion context from multiple o...

Full description

Saved in:
Bibliographic Details
Main Authors: Ju Hong Yoon, Ming-Hsuan Yang, Jongwoo Lim, Kuk-Jin Yoon
Format: Conference Proceeding
Language:English
Subjects:
Bayes methods
Cameras
Data models
Predictive models
Robustness
Tracking
Trajectory
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Online multi-object tracking with a single moving camera is a challenging problem as the assumptions of 2D conventional motion models (e.g., first or second order models) in the image coordinate no longer hold because of global camera motion. In this paper, we consider motion context from multiple objects which describes the relative movement between objects and construct a Relative Motion Network (RMN) to factor out the effects of unexpected camera motion for robust tracking. The RMN consists of multiple relative motion models that describe spatial relations between objects, thereby facilitating robust prediction and data association for accurate tracking under arbitrary camera movements. The RMN can be incorporated into various multi-object tracking frameworks and we demonstrate its effectiveness with one tracking framework based on a Bayesian filter. Experiments on benchmark datasets show that online multi-object tracking performance can be better achieved by the proposed method.
ISSN:1550-5790
2642-9381
DOI:10.1109/WACV.2015.12