Visual tracking of multiple targets by multi-bernoulli filtering of background subtracted image data

Authors:
Reza Hoseinnezhad;Ba-Ngu Vo;Truong Nguyen Vu
Affiliations:
RMIT University, Victoria, Australia;The University of Western Australia, WA, Australia;Vietnam Academy of Science and Technology, Ho Chi Minh City, Vietnam
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

Citing 11
Cited 0

Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors

International Journal of Computer Vision
Tracking multiple objects through occlusion with online sampling and position estimation

Pattern Recognition
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Tracking of Multiple Humans in Crowded Environments

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Object Tracking Based on Adaptive Depth Segmentation

CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Multiple target tracking with lazy background subtraction and connected components analysis

Machine Vision and Applications
Statistical Multisource-Multitarget Information Fusion

Statistical Multisource-Multitarget Information Fusion
Closed-world tracking of multiple interacting targets for indoor-sports applications

Computer Vision and Image Understanding
The cardinality balanced multi-target multi-Bernoulli filter and its implementations

IEEE Transactions on Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing - Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Most visual multi-target tracking techniques in the literature employ a detection routine to map the image data to point measurements that are usually further processed by a filter. In this paper, we present a visual tracking technique based on a multi-target filtering algorithm that operates directly on the image observations and does not require any detection nor training patterns. Instead, we use the recent history of image data for non-parametric background subtraction and apply an efficient multi-target filtering technique, known as the multi-Bernoulli filter, on the resulting grey scale image data. In our experiments, we applied our method to track multiple people in three video sequences from the CAVIAR dataset. The results show that our method can automatically track multiple interacting targets and quickly finds targets entering or leaving the scene.