Dynamic shortest path association for multiple object tracking in video sequence

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.

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Novel Adaptive Background Segmentation Algorithm for Multiple Object Tracking

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.27.17984 ◽

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.

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All Pairs Shortest Path Formulation for Multiple Object Tracking with Application to Tennis Video Analysis

Procedings of the British Machine Vision Conference 2007 ◽

10.5244/c.21.69 ◽

2007 ◽

Cited By ~ 2

Author(s):

F. Yan ◽

B. Christmas ◽

J. Kittler

Keyword(s):

Object Tracking ◽

Shortest Path ◽

Video Analysis ◽

Multiple Object Tracking ◽

Multiple Object ◽

All Pairs Shortest Path

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Multiple-Object Tracking

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000085 ◽

2011 ◽

Vol 58 (3) ◽

pp. 196-200 ◽

Cited By ~ 15

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.

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