Depth assisted occlusion handling in video object tracking

Authors:
Yingdong Ma;Qian Chen
Affiliations:
Centre for Digital Media Computing, Shenzhen Institutes of Advanced Technology, Shenzhen, China;Centre for Digital Media Computing, Shenzhen Institutes of Advanced Technology, Shenzhen, China
Venue:
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Year:
2010

Citing 10
Cited 0

Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Matching for Persistent Tracking with Heterogeneous Features

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereovision-based object segmentation for automotive applications

EURASIP Journal on Applied Signal Processing
Tracking multiple objects through occlusion with online sampling and position estimation

Pattern Recognition
Multiple Object Tracking Based on Adaptive Depth Segmentation

CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
On Color-, Infrared-, and Multimodal-Stereo Approaches to Pedestrian Detection

IEEE Transactions on Intelligent Transportation Systems
Robust online appearance models for visual tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast occluded object tracking by a robust appearance filter

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking video objects in cluttered background

IEEE Transactions on Circuits and Systems for Video Technology
Robust and Accurate Object Tracking Under Various Types of Occlusions

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a depth assisted video object tracking algorithm that utilizes a stereo vision technique to detect and handle various types of occlusions. The foreground objects are detected by using a depth and motion-based segmentation method. The occlusion detection is achieved by combining the depth segmentation results with the previous occlusion status of each track. According to the occlusion analysis results, different object correspondence algorithms are employed to track objects under various occlusions. The silhouette-based local best matching method deals with severe and complete occlusions without assumptions of constant movement and limited maximum duration. Experimental results demonstrate that the proposed system can accurately track multiple objects in complex scenes and provides improvements on dealing with different partial and severe occlusion situations.