Local depth patterns for tracking in depth videos

Awwad, S; Hussein, F; Piccardi, M

Local depth patterns for tracking in depth videos

Awwad, S Hussein, F Piccardi, M

Permalink

Publication Type:: Conference Proceeding
Citation:: MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 1115 - 1118
Issue Date:: 2015-10-13

Closed Access

	Filename	Description	Size
	369-submission-camera-ready-ver.pdf	Accepted Manuscript version	605.7 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Awwad, S	en_US
dc.contributor.author	Hussein, F	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.available	2015-07-05	en_US
dc.date.issued	2015-10-13	en_US
dc.identifier.citation	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference, 2015, pp. 1115 - 1118	en_US
dc.identifier.isbn	9781450334594	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37829
dc.identifier.uri	http://hdl.handle.net/10453/37678
dc.description.abstract	© 2015 ACM. Conventional video tracking operates over RGB or grey-level data which contain significant clues for the identification of the targets. While this is often desirable in a video surveillance context, use of video tracking in privacy-sensitive environments such as hospitals and care facilities is often perceived as intrusive. Therefore, in this work we present a tracker that provides effective target tracking based solely on depth data. The proposed tracker is an extension of the popular Struck algorithm which leverages a structural SVM framework for tracking. The main contributions of this work are novel depth features based on local depth patterns and a heuristic for effectively handling occlusions. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Stuck tracker and other state-of-The-Art trackers using depth and RGB data.	en_US
dc.relation.ispartof	MM 2015 - Proceedings of the 2015 ACM Multimedia Conference	en_US
dc.relation.isbasedon	10.1145/2733373.2806295	en_US
dc.title	Local depth patterns for tracking in depth videos	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015 ACM. Conventional video tracking operates over RGB or grey-level data which contain significant clues for the identification of the targets. While this is often desirable in a video surveillance context, use of video tracking in privacy-sensitive environments such as hospitals and care facilities is often perceived as intrusive. Therefore, in this work we present a tracker that provides effective target tracking based solely on depth data. The proposed tracker is an extension of the popular Struck algorithm which leverages a structural SVM framework for tracking. The main contributions of this work are novel depth features based on local depth patterns and a heuristic for effectively handling occlusions. Experimental results over the challenging Princeton Tracking Benchmark (PTB) dataset report a remarkable accuracy compared to the original Stuck tracker and other state-of-The-Art trackers using depth and RGB data.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/37678