A* algorithm with dynamic weights for multiple object tracking in video sequence

Optik ◽  
2015 ◽  
Vol 126 (20) ◽  
pp. 2500-2507 ◽  
Author(s):  
Zhenghao Xi ◽  
Dongmei Xu ◽  
Wanqing Song ◽  
Yang Zheng
Keyword(s):  
Object Tracking ◽  
Video Sequence ◽  
Multiple Object Tracking ◽  
A Algorithm ◽  
Multiple Object

A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

2012 ◽  
Vol 37 (1) ◽  
pp. 47-67 ◽  
Author(s):  
Rafael M. Luque-Baena ◽  
Juan M. Ortiz-de-Lazcano-Lobato ◽  
Ezequiel López-Rubio ◽  
Enrique Domínguez ◽  
Esteban J. Palomo
Keyword(s):  
Neural Network ◽  
Sequence Analysis ◽  
Object Tracking ◽  
Video Sequence ◽  
Multiple Object Tracking ◽  
Competitive Neural Network ◽  
Multiple Object ◽  
Video Sequence Analysis

Dynamic shortest path association for multiple object tracking in video sequence

2015 ◽  
Vol 24 (1) ◽  
pp. 013009 ◽  
Author(s):  
Zhenghao Xi ◽  
Heping Liu ◽  
Huaping Liu ◽  
Yang Zheng
Keyword(s):  
Object Tracking ◽  
Shortest Path ◽  
Video Sequence ◽  
Multiple Object Tracking ◽  
Multiple Object

scholarly journals Novel Adaptive Background Segmentation Algorithm for Multiple Object Tracking

2018 ◽  
Vol 7 (3.27) ◽  
pp. 407
Author(s):  
V Ramalakshmi @ Kanthimathi ◽  
M Germanus Alex
Keyword(s):  
Object Tracking ◽  
Video Sequence ◽  
Multiple Object Tracking ◽  
Vital Role ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Multiple Objects ◽  
Multiple Object ◽  
Background Segmentation

Multiple object tracking plays a vital role in many applications. The objective of this paper is to track multiple objects in all the scenes of the video sequence. In this paper, an algorithm is proposed to identify objects between scenes by dividing the scenes in the video sequence. Within each scene, objects are identified and tracked between scenes by segmenting the background adaptively. The proposed method is tested on four publicly available datasets. The experimental results substantially proved that the proposed method achieves better performance than other recent 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.

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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