Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine

Jemilda, G.; Baulkani, S.

doi:10.15837/ijccc.2018.2.3064

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…Through the Center Net target detection technology, the game skill detection, tactical analysis, and diagnosis in tennis matches are used as the judging criteria. Center Net provides a simpler and more efficient object detection algorithm [15]. The main idea is as follows: the target frame is represented by a single point in the center of the target frame, and other attributes, such as target size, dimension, 3D range, orientation, and object pose, are regressed through image features at the position of the center point [16].…”

Section: Tennis Net Recognition Technology In Machine Learningmentioning

confidence: 99%

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

Liu

Huang

2022

Wireless Communications and Mobile Computing

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Tennis competition is one of sports events. The vigorous development of sports can promote people’s pursuit of sports spirit and promote social unity and stability. However, in the course of social modernization development, sports events also need to follow closely, and we can no longer rely on traditional tools to support the development of events, which is not conducive to the positive development of sports. Due to the problem that the number of times the ball crosses the net in the traditional tennis game is too backward and the error rate is high, this paper uses artificial intelligence technology to record the number of times the ball crosses the net in tennis games and introduces the Center Net target based on the diagnostic criteria and comprehensive evaluation of tennis nets. Detect and track tennis balls by target recognition, feature extraction, and other methods, and record the times of net passing. However, considering that abnormal behaviors will also occur in the process of tennis passing through the net, the density-based DBSCAN clustering algorithm is used to discriminate and record abnormal behaviors in tennis matches. In order to verify the detection performance of DBSCAN clustering algorithm and Center Net target detection, the video of tennis match was analyzed and compared by drone from the perspective of time and space. By recording the number of times the ball crosses the net during the tennis receiving, connecting and attacking, and stalemate phases, the performance of the two recording times is compared. R 2 are all higher than 0.94, and R values are 0.982 and 0.963, respectively, so the recall rate of DBSCAN clustering algorithm is 0.02 higher, which is better than Center Net target detection. Using artificial intelligence technology to record the number of times the ball crosses the net in tennis games can not only improve the scientificity and accuracy of the record but also promote the development of the sports industry.

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Section: Tennis Net Recognition Technology In Machine Learningmentioning

confidence: 99%

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

Liu

Huang

2022

Wireless Communications and Mobile Computing

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“…Among them is Jemilda’s and Baulkani’s research that uses genetic clustering algorithm to explore the range of motion kernel fuzzy mean value in moving target detection and tracking technology. A moving target kernel fuzzy c-means algorithm based on genetic clustering algorithm for cluster analysis of moving target trajectory is proposed 1 . Yazdi’s and Bouwmans’s research mainly introduces the latest methods for detection and tracking of moving objects in video sequences captured by moving cameras.…”

Section: Related Workmentioning

confidence: 99%

“…A moving target kernel fuzzy c-means algorithm based on genetic clustering algorithm for cluster analysis of moving target trajectory is proposed. 1 Yazdi's and Bouwmans's research mainly introduces the latest methods for detection and tracking of moving objects in video sequences captured by moving cameras. The challenges and main concerns in the field of moving object detection and tracking technology are presented, as well as some benchmark databases that can be used to evaluate the performance of different moving object detection and tracking algorithms.…”

Section: Related Workmentioning

confidence: 99%

(Retracted) Detection and tracking of moving targets in martial arts sports arena video based on non-scene target features

Jia

Wang

2022

J. Electron. Imag.

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.With the vigorous development of contemporary information technology, the field of computer technology is constantly innovating and progressing. Among them, the field of computer vision has achieved more prominent development. Computer vision is a discipline that uses cameras and computers to imitate the structure of human organs, processes and analyzes the collected video sequences, and then makes corresponding behavior judgments. Moving object detection and tracking is an important branch of computer vision. Moving target detection and tracking has broad application space and development scenarios. Here, we aim to study the detection and tracking method of moving targets in a martial arts arena video based on non-scene target features. Based on this research theme, we combine the particle filter moving target tracking algorithm based on non-scene target features to carry out the moving target detection and tracking experiments in the video of the martial arts arena. The experiment concluded: compared with the conventional methods, the particle filter moving target tracking algorithm based on the non-scene target features can perform the following tasks without affecting the detection accuracy. It reduces the time for detecting and tracking the same moving target in the video of the martial arts arena by 1.5 min. It shows that the algorithm can improve the efficiency of non-scene moving target detection and tracking.

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“…GA [16][17][18][19][20] is a stochastic search algorithm for optimal solution. In the search process, each population is selected according to individual fitness, and subjected to crossover and mutation, creating the next generation of population.…”

Section: Inverse Scheduling Algorithm Based On Ga and Psomentioning

confidence: 99%

A Solution to Single-Machine Inverse Job-Shop Scheduling Problem

Wang¹,

Yang²,

Wang³

2019

Int. j. simul. model.

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Concerning the inverse job-shop scheduling problem (JSP), this paper proposes a hybrid solution based on genetic algorithm (GA) and improved particle swarm optimization (PSO), with the aim to minimize the parameter adjustment. The solution was presented as a block coding plan with decimal mechanism, under which both processes and parameters can be optimized simultaneously. To enhance the local search ability of the proposed algorithm, four neighbourhood structures were designed, and an adaptive selection mechanism was created to select the most suitable neighbourhood. Finally, the proposed algorithm was proved valid through discrete event simulation (DES) and comparison with other algorithms.

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Moving Object Detection and Tracking using Genetic Algorithm Enabled Extreme Learning Machine

Cited by 10 publications

References 7 publications

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

Artificial Intelligence Technology to Record the Number of Times the Ball Passes the Net in Tennis Matches

(Retracted) Detection and tracking of moving targets in martial arts sports arena video based on non-scene target features

A Solution to Single-Machine Inverse Job-Shop Scheduling Problem

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