A trajectory-based ball detection and tracking system with applications to shooting angle and velocity estimation in basketball videos

Chakraborty, Bodhisattwa; Meher, Sukadev

doi:10.1109/indcon.2013.6725963

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…There are numerous automatic foreground extraction methods for a one frame processing [55][56][57][58] or sequence frames processing [59][60][61][62]. [55] employed a histogram learning technique to detect the background pixels.…”

Section: Related Workmentioning

confidence: 99%

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Abulwafa

Saleh

Saraya

et al. 2021

Int J of Intelligent Sys

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Sports video analysis has received much attention as it is turned to be a hot research area in the field of image processing. This led to opportunities to develop fascinating applications supported by analysis of different sports especially football. Identifying the ball in soccer images is an essential task for not only goal scoring but also players' evaluation. However, soccer ball detection suffers from several hurdles such as; occlusions, fast moving objects, shadows, poor lighting, color contrast, and other static background objects. Although several ball detection techniques have been introduced such as; Frame Difference, Mixture of Gaussian (MoG), Optical Flow and etc., ball detection in soccer games is still an open research area. In this paper, a new Fuzzy Based Ball Detection (FB 2 D) strategy is proposed for identifying the ball through a set of image sequences extracted form a soccer match video. FB 2 D has the ability to accurately identify the ball even if it is attached to the white lines drawn on the playground or partially occluded behind players. FB 2 D has been compared to recent ball detection techniques. Experimental results have shown that FB 2 D outperforms recent detection techniques as it introduced the maximum accuracy and the accuracy of detection in the testing stage is close to 100%. As well as the minimum error.

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Section: Related Workmentioning

confidence: 99%

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Abulwafa

Saleh

Saraya

et al. 2021

Int J of Intelligent Sys

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“…To make it autonomous, JungSoo Lee et al, 2018 [16] had presented the technique in basketball game. Bodhisattwa Chakraborty and Sukadev Meher, 2013 [17] presented the video sequence used a trajectory-based ball tracking method to review the long shot. The trajectory of the ball had been determined along with it, the velocity and angle of ball thrown had been determined.…”

Section: Literature Surveymentioning

confidence: 99%

Basketball Tracking using Mean Shift Algorithm

Margarat¹,

Sivasubramanian²

2019

IJRTE

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Video analysis plays a vital role in commercial application, sports and military systems. Various methods are presented in literature. Mean shift algorithm is presented in this paper for basket ball tracking because it is more efficient than other that is defined by histograms. The tracking is the important block in the detection and recognition of the basket ball. Different object tracking algorithms are investigated. The performance of tracking in two video sequences is performed and the method gives 91.3% precision for video sequence 1 and 93.6% for sequence 2

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“…There are many automatic foreground extraction methods, for one frame processing [1][2][3][4][5] or sequence frames processing [6][7][8][9]. Tong et al [1] performed field extraction based on the color distance between a pixel and the mean value of the field.…”

Section: Introductionmentioning

confidence: 99%

“…Chakraborty et al [6] employed a background subtraction method along with a frame differencing method using three consecutive frames were employed for segmentation on the moving objects in the foreground. Durus [7] accomplished the segmentation of background by using the dominant color distribution.…”

Section: Introductionmentioning

confidence: 99%

Real time object localization based on histogram of s-RGB

Mudjirahardjo

Suyono

Setyawan

2017

AIP Conference Proceedings

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Abstract. Object localization is the first task in pattern detection and recognition. This task is very important due to it reduces the searching time to the interest object. In this paper we introduce our novel method of object localization based on color feature. Our novel method is a histogram of s-RGB. This histogram is used in the training phase to determine the color dominant in the initial Region of Interest (ROI). Then this information is used to label the interest object. To reduce noise and localize the interest object, we apply the row and column density function of pixels. The comparison result with some processes, our system gives a best result and takes a short computation time of 26.56 ms, in the video rate of 15 frames per second (fps).

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A trajectory-based ball detection and tracking system with applications to shooting angle and velocity estimation in basketball videos

Cited by 19 publications

References 9 publications

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

A new ball detection strategy for enhancing the performance of ball bees based on fuzzy inference engine

Basketball Tracking using Mean Shift Algorithm

Real time object localization based on histogram of s-RGB

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