Object detection using Metaheuristic algorithm for volley ball sports application

Balaji, S.; Karthikeyan, S.; Manikandan, R.

doi:10.1007/s12652-020-01981-5

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…[49] used a color classifier to create color categories and proposed the concept of category probability. [54] Proposed a Metaheuristic algorithm for the detection of the sports objects from the video sequence.…”

Section: Related Workmentioning

confidence: 99%

“…The experimental result showed that the developed Unified model method has the highest performance in the object detection in comparison to the other methods. A Metaheuristic algorithm for the detection of sports objects from the video sequence is proposed in [54]; such algorithm also rectified the challenges in object detection due to shadows and lighting. The results showed that Cuckoo Search Algorithm could give better accuracy and precision with respect to recall than the other algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…Experiments were carried out on different image sequences, which are taken in matches played in FIFA World Cup Russia 2018. Our algorithm has been compared to six different algorithms, as shown in Table 5; they are Modified Atherton Algorithm (MAA) [50], Scale-Invariant Feature Transform based Algorithm (SIFT) [21], Speeded Up Robust Features based Algorithm (SURF) [51], Entropy-Based initial Background Extraction (EBBE) Algorithm [52], Improved Non-Maximum Suppression (INMS) [53] and Cuckoo Search Algorithm [54]. Supposing that the camera of the quadcopter is at a fixed height, the ball will correspond to a circular region with radii in the range (RMIN = 24, RMAX = 28) depending on the distance of the ball with respect to the optical center of the camera.…”

Section: Competitors and The Evaluation Image Sequencesmentioning

confidence: 99%

See 2 more Smart Citations

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%

Section: Related Workmentioning

confidence: 99%

Section: Competitors and The Evaluation Image Sequencesmentioning

confidence: 99%

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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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“…Despite considerable advances in computer vision, object detection is still an active topic of study [1][2][3][4]. This process is used in many fields, such as biomedical imaging, biometry, video surveillance, vehicle navigation, visual inspection, robot navigation, and remote sensing [1][2][3][4][5], to mention a few. Object identification has been considered an essential task and one of the biggest challenges in image processing [1][2][3][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…This process is used in many fields, such as biomedical imaging, biometry, video surveillance, vehicle navigation, visual inspection, robot navigation, and remote sensing [1][2][3][4][5], to mention a few. Object identification has been considered an essential task and one of the biggest challenges in image processing [1][2][3][6][7][8]. Several object recognition problems are solved utilizing digital image processing techniques, where segmentation methods are essential procedures [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

et al. 2021

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Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

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Cricket Scene Analysis Using the RetinaNet Architecture

Moodley

Haar

2021

Lecture Notes in Computer Science

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Object detection using Metaheuristic algorithm for volley ball sports application

Cited by 8 publications

References 14 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

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

Cricket Scene Analysis Using the RetinaNet Architecture

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