A Method for Object Tracking using Shape Matching

2006 ◽  
Author(s):  
Qi Xiaoping ◽  
Zhang Qiheng ◽  
Ouyang Yimin ◽  
Ma Jiaguang
Keyword(s):  
Object Tracking ◽  
Shape Matching

DETECTING GHOST AND LEFT OBJECTS IN SURVEILLANCE VIDEO

2009 ◽  
Vol 23 (07) ◽  
pp. 1503-1525 ◽  
Author(s):  
SIJUN LU ◽  
JIAN ZHANG ◽  
DAVID DAGAN FENG
Keyword(s):  
Object Tracking ◽  
Shape Matching ◽  
Detection Method ◽  
Input Image ◽  
Second Step ◽  
Surveillance Systems ◽  
Surveillance Video ◽  
Stationary Objects ◽  
Continuous Object ◽  
Background Image

This paper proposes an efficient method for detecting ghost and left objects in surveillance video, which, if not identified, may lead to errors or wasted computational power in background modeling and object tracking in video surveillance systems. This method contains two main steps: the first one is to detect stationary objects, which narrows down the evaluation targets to a very small number of regions in the input image; the second step is to discriminate the candidates between ghost and left objects. For the first step, we introduce a novel stationary object detection method based on continuous object tracking and shape matching. For the second step, we propose a fast and robust inpainting method to differentiate between ghost and left objects by reconstructing the real background using the candidate's corresponding regions in the current input and background image. The effectiveness of our method has been validated by experiments over a variety of video sequences and comparisons with existing state-of-art methods.

Multiple-Object Tracking

2011 ◽  
Vol 58 (3) ◽  
pp. 196-200 ◽  
Author(s):  
K. Botterill ◽  
R. Allen ◽  
P. McGeorge
Keyword(s):  
Object Tracking ◽  
Cognitive Processes ◽  
Spatial Location ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

Predictable paths increase the use of motion in object tracking

2010 ◽  
Author(s):  
Adriane E. Seiffert ◽  
Rebecca St. Clair
Keyword(s):  
Object Tracking

Location and identity are entangled in multiple-object tracking

2010 ◽  
Author(s):  
Todd S. Horowitz ◽  
Michael A. Cohen ◽  
Yair Pinto ◽  
Piers D. L. Howe
Keyword(s):  
Object Tracking ◽  
Multiple Object Tracking ◽  
Multiple Object
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