Arbitrarily colored ball detection using the structure tensor technique

Wenig, Mark; Pang, Ken C; On, Peter

doi:10.1016/j.mechatronics.2010.07.005

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Because the main difference between the noise area and the target area is the value of component S of the pixels in the noise area is less, the high pass filter can be used to filter the pixels of less values of component S. Usually the truncation threshold of high pass filter is set fixedly according to people's subjective judgments. If the truncation threshold of high pass filter is applied to Camshift algorithms, the following qualification (1) needs to be added to filter out pixels to generate target template [5]. The smin is the minimum value of component S, the vmin is the minimum value of component V and the vmax is the maximum value of component V. Because these values are set according to people's subjective judgments, there is a large degree of uncertainty and delay, which cannot ensure the accuracy and the real-time performance of the filtered pixels.…”

Section: B Image Filtering Processingmentioning

confidence: 99%

A New Improved Camshift Algorithm and Its Experimental Analysis

Wang¹,

Xiao²,

Liu³

et al. 2015

Proceedings of the 2015 International Conference on Electronic Science and Automation Control

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Abstract-Camshift is an adaptive tracking algorithm based on color histogram with the advantages of translation, scale and rotation invariance, which is widely used in visual tracking. But in the moving scenes, due to the changing background, occlusion and environmental illumination, the tracking effectiveness and efficiency will be affected and even lead to loss of moving target. Combined with the idea of both bottom-up and top-down, this paper suggests an improved one by using pixel filtering, histogram improvement and occlusion judgment, as well as Kalman filter. The comparative analysis results on the traditional Camshift algorithm and combined with Kalman traditional Camshift algorithm demonstrate that the proposed algorithm enhances the accuracy and stability in complex condition in an effective way and improves the real-time performance as well.

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Section: B Image Filtering Processingmentioning

confidence: 99%

A New Improved Camshift Algorithm and Its Experimental Analysis

Wang¹,

Xiao²,

Liu³

et al. 2015

Proceedings of the 2015 International Conference on Electronic Science and Automation Control

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“…In recent years, object recognition without any colour classification, especially the recognition of arbitrary FIFA balls, has become a research focus in the robot vision of MSL [34][35][36][37][38][39][40][41][42][43][44]. A so-called Contracting Curve Density (CCD) algorithm [34][35][36] was proposed by Hanek et al to recognize soccer balls without colour labelling.…”

Section: Image Processing Analysis and Understandingmentioning

confidence: 99%

“…Bonarini et al used a circular Hough transform on the edges extracted from a colour invariant transformation algorithm to detect the generic ball and a Kalman Filter was also applied to track and predict the position of the ball in the next image to reduce the computational load [41]. An advanced version of the Hough transform was proposed to detect the ball without colour information by using the structure tensor technique in [42], but this method is time consuming and cannot be run in real-time.…”

Section: Image Processing Analysis and Understandingmentioning

confidence: 99%

A Survey on Visual Perception for RoboCup MSL Soccer Robots

Xiong

et al. 2013

International Journal of Advanced Robotic Systems

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Visual perception is the most important method for providing information about the competition environment for RoboCup Middle Size League (MSL) soccer robots. The paper reviews the advancement of visual perception in RoboCup MSL soccer robots from several points of view including the design and calibration of the vision system, the visual object recognition, the estimation of the object’s motion, robot visual self‐localization and multi‐robot cooperative sensing. The research progress we have achieved is also introduced in this review. The developing trends and the future research focuses on this problem are also discussed

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“…Bonarini et al used a circular Hough transform on the edges extracted from a colour invariant transformation algorithm to detect the generic ball, and a Kalman Filter was also applied to track and predict the position of the ball in the next image to reduce the computational load [10]. An advanced version of the Hough transform was proposed to detect the ball without colour information by using the structure tensor technique in [11], but this method is time consuming and it cannot be run in real-time.…”

Section: Related Researchmentioning

confidence: 99%

A Novel Generic Ball Recognition Algorithm Based on Omnidirectional Vision for Soccer Robots

Zhang

Dong

et al. 2013

International Journal of Advanced Robotic Systems

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It is significant for the final goal of RoboCup to realize the recognition of generic balls for soccer robots. In this paper, a novel generic ball recognition algorithm based on omnidirectional vision is proposed by combining the modified Haar-like features and AdaBoost learning algorithm. The algorithm is divided into offline training and online recognition. During the phase of offline training, numerous sub-images are acquired from various panoramic images, including generic balls, and then the modified Haar-like features are extracted from them and used as the input of the AdaBoost learning algorithm to obtain a classifier. During the phase of online recognition, and according to the imaging characteristics of our omnidirectional vision system, rectangular windows are defined to search for the generic ball along the rotary and radial directions in the panoramic image, and the learned classifier is used to judge whether a ball is included in the window. After the ball has been recognized globally, ball tracking is realized by integrating a ball velocity estimation algorithm to reduce the computational cost. The experimental results show that good performance can be achieved using our algorithm, and that the generic ball can be recognized and tracked effectively.

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Arbitrarily colored ball detection using the structure tensor technique

Cited by 4 publications

References 9 publications

A New Improved Camshift Algorithm and Its Experimental Analysis

A New Improved Camshift Algorithm and Its Experimental Analysis

A Survey on Visual Perception for RoboCup MSL Soccer Robots

A Novel Generic Ball Recognition Algorithm Based on Omnidirectional Vision for Soccer Robots

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