A moving object tracking based on color information employing a particle filter algorithm

Sugandi, Budi; Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji

doi:10.1007/s10015-009-0718-6

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…However, in the event of the occlusion, as information is lost, this technique will find it difficult to track the occluded object. To overcome the problem (partial and full occlusion), several researchers have proposed various methods such as particle filter with the Gaussian process dynamical model [50], integration of particle filter with data association based tracker [51], multiple parallel trackers using multiple particle filter [52], and color-based particle filter method [53]. However, data test sequences used in their study only involved linear motion patterns.…”

Section: A Particle Filtermentioning

confidence: 99%

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Musa

Salleh

Bakar

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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Section: A Particle Filtermentioning

confidence: 99%

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

Musa

Salleh

Bakar

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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“…The merge situation of objects fires the event E 5 . Triggering E 5 depends on a sudden change of an object size to larger size and the prediction of intersecting elements in KBB.…”

Section: Camera-05mentioning

confidence: 99%

“…The first is the high preciseness scenario, which can be used in gait recognition to get the motion pattern of an object [3]. The second is the medium preciseness one, which can be used in recognizing generic activities such as walk, run, or slide [4,5]. The third is the low-preciseness scenario, which is used to detect the presence of the object in crowded environment.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Event-Driven Strategy for Real-Time Multiple Object Tracking Using Static Cameras

Sarhan

Saleh

Elsadek

2011

Advances in Multimedia

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Recently, because of its importance in computer vision and surveillance systems, object tracking has progressed rapidly over the last two decades. Researches on such systems still face several theoretical and technical problems that badly impact not only the accuracy of position measurements but also the continuity of tracking. In this paper, a novel strategy for tracking multiple objects using static cameras is introduced, which can be used to grant a cheap, easy installation and robust tracking system. The proposed tracking strategy is based on scenes captured by a number of static video cameras. Each camera is attached to a workstation that analyzes its stream. All workstations are connected directly to the tracking server, which harmonizes the system, collects the data, and creates the output spatial-tempo database. Our contribution comes in two issues. The first is to present a new methodology for transforming the image coordinates of an object to its real coordinates. The second is to offer a flexible event-based object tracking strategy. The proposed tracking strategy has been tested over a CAD of soccer game environment. Preliminary experimental results show the robust performance of the proposed tracking strategy.

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“…Recently, the particle filter has proven very successful for nonlinear and non-Gaussian estimation problems [4][5][6][7][8]. It approximates a posterior probability density of the state such as the object position by using samples which are called particles.…”

Section: Introductionmentioning

confidence: 99%

Object Tracking Based on Fuzzy Information Employing a Particle Filter Algorithm

Jafari

Tossizadeh

Manesh

2012

2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems

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In this article, we present a new algorithm to track a moving object based on fuzzy histogram information employing a particle filter algorithm. Recently a particle filter has been proven very successful for nonlinear and nonGaussian estimation problems. It approximates a posterior probability density of the state, such as the object position, by using samples which are called particles. Also, a fuzzy method of color and edge histograms is used. For likelihood, we consider the similarity between the fuzzy histograms of the tracked object and the region around the position of each particle with a Stochastic feature Fusion Scheme. The Bhattacharya distance is used to measure this similarity. The mean state of the particles is treated as the estimated position of the object. The experiment shows that the proposed method has strong tracking robustness and can effectively solve this problem.

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A moving object tracking based on color information employing a particle filter algorithm

Cited by 12 publications

References 5 publications

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

GbLN-PSO and Model-Based Particle Filter Approach for Tracking Human Movements in Large View Cases

A Reliable Event-Driven Strategy for Real-Time Multiple Object Tracking Using Static Cameras

Object Tracking Based on Fuzzy Information Employing a Particle Filter Algorithm

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