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Paper TuC08.1

Li, Yanli (Beihang University), Zhou, Zhong (Beihang University), Wu, Wei (Beihang University)

Iterative Pedestrian Segmentation and Pose Tracking under a Probabilistic Framework

Scheduled for presentation during the Regular Session "Human Detection and Tracking" (TuC08), Tuesday, May 15, 2012, 14:30−14:45, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Visual Tracking, Human Detection & Tracking, Computer Vision for Robotics and Automation

Abstract

This paper presents a unified probabilistic framework to tackle two closely related visual tasks: pedestrian segmentation and pose tracking along monocular videos. Although the two tasks are complementary in nature, most previous approaches focus on them individually. Here, we resolve the two problems simultaneously by building and inferring a single body model. More specifically, pedestrian segmentation is performed by optimizing body region with constraint of body pose in a Markov Random Field (MRF), and pose parameters are reasoned about through a Bayesian filtering, which takes body silhouette as an observation cue. Since the two processes are inter-related, we resort to an Expectation-Maximization (EM) algorithm to refine them alternatively. Additionally, a template matching scheme is utilized for initialization. Experimental results on challenging videos verify the framework's robustness to non-rigid human segmentation, cluttered backgrounds and moving cameras.

 

 

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