Real time occlusion handling using Kalman Filter and mean-shift

Panahi, Rahim; Gholampour, Iman; Jamzad, Mansour

doi:10.1109/iranianmvip.2013.6780003

Cited by 9 publications

(8 citation statements)

References 11 publications

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“…(2).MeanShift is an optimal algorithm which is utilized to search local area using the gradient climb of probability density [4] . We can obtain the hand's position and size according to the repeatedly iterative calculation of MeanShift algorithm.…”

Section: Tracking Of the Handmentioning

confidence: 99%

“…Otherwise, the computed result that hand tracking is abnormal using CamShift is effected by other colors. Additionally, we cannot predict the next frame's position accurately using CamShift to update Kalman filter [4] , which result in failing to track.We utilize the predicted value computed by Kalman filter as the initialized searching window of CamShift algorithm and figure out the accurate hand position. Then we utilize the position value to update Kalman filter.…”

Section: Tracking Of the Handmentioning

confidence: 99%

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Detection and tracking of the hand in remote chess game system

Wang¹,

Xing²

2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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Abstract. Remote chess game system use the network transmission to let the rival chess image transfor to our own computer.At the same time,the virtual chess pieces that represent our own chess pieces are in the rival chess image.The image on monitor show the two sides of chess pieces real-timely.The detection and tracking of the hand has very important significance to detect the movement of chess pieces and the effect of the system.In this paper,an algorithm based on skin color detection and inter-frame difference is used to detect the position of hand and this position is the initial search window in Camshift.This algorithm can detect the position of hand automatically. Kalman filter is used to predict to solve the color interference question that may track the wrong position.The results show that this algorithm can detect and track the hand accurately and real-timely.

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Section: Tracking Of the Handmentioning

confidence: 99%

Section: Tracking Of the Handmentioning

confidence: 99%

Detection and tracking of the hand in remote chess game system

Wang¹,

Xing²

2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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show abstract

“…It leads to high-level processing such as recognition, clustering and re-identification. Tracking algorithm can be classified into two major groups, namely state space approach such as Particle Filter or Kalman Filter and kernel based approach like Mean Shift algorithm [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Object tracking with occlusion handling using mean shift, Kalman filter and Edge Histogram

Iraei

Faez

2015

2015 2nd International Conference on Pattern Recognition and Image Analysis (IPRIA)

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This paper propose an algorithm that uses Mean Shift and Kalman Filter for object tracking. Also this method uses Edge Histogram for occlusion handling. Firstly, we use Mean Shift algorithm to obtain center of desired object. But the robust of tracking is not very well, so we use Kalman Filter to improve the effect of tracking. Bhattacharyya coefficient and Edge Histogram are used for finding out both partial and full occlusions. With this approach we can track the object more accurately. The results prove that the robust of tracking is very well.

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“…Mean Shift algorithm had been used for robust object tracking from occlusion [3,4,5], however object tracking was not robust from background interference. Comaniciu et al developed Mean Shift method to reduce background interference feature using BWH (Background Weighted Histogram) [6], however this method could not improve Mean Shift method, because the transformation of the target model and target candidate representation models have same value as the original Mean Shift, so there was no changes.…”

Section: Introductionmentioning

confidence: 99%

Visual object tracking using improved Mean Shift algorithm

Setyawan

Purwanto

Mardiyanto

2015

2015 International Conference on Information Technology Systems and Innovation (ICITSI)

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Visual object tracking is one of many important applications for surveillance systems. The issues for visual object tracking are robustness from background interference, scaling and occlusion detection. In this paper, visual object tracking using improved Mean Shift algorithm is proposed. Mean Shift algorithm is used to obtain center object target for tracking. Corrected Background Weighted Histogram is added in target model to reduce background interference. Then, Scale adaptive methods is added in Mean Shift for scaling. Occlusion detection is handled by scaled Normalized Cross Correlation. The results prove that the proposed method is robust from noise background, scaling and occlusion detection.

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Real time occlusion handling using Kalman Filter and mean-shift

Cited by 9 publications

References 11 publications

Detection and tracking of the hand in remote chess game system

Detection and tracking of the hand in remote chess game system

Object tracking with occlusion handling using mean shift, Kalman filter and Edge Histogram

Visual object tracking using improved Mean Shift algorithm

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