Sport video analysis is gaining popularity recently owing to its importance in understanding sports and improving the performance of athletes. In this paper we focus on shuttlecock tracking algorithm. Particularly, a ...
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Sport video analysis is gaining popularity recently owing to its importance in understanding sports and improving the performance of athletes. In this paper we focus on shuttlecock tracking algorithm. Particularly, a novel fast tracking based on object center (i.e., FTOC) method by fusing heterogeneous cues and AdaBoost algorithm are proposed to improve the tracking performance for a robot. Experimental results show that the proposed FTOC tracking method performs favorably against many other popular tracking approaches, such as TLD, MIL, KCF, DCF_CA, SMAF_CA, KCC, DSN, COKCF, etc., in term of speed, accuracy, and robustness, especially in challenging scenarios such as scale variations and background clutter. We further demonstrate the feasibility of the FTOC algorithm in a real-time ZED binocular camera based 3D shuttlecock tracking system for a robot. (C) 2019 Elsevier B.V. All rights reserved.
The ability to identify objects of interest from digital visual signals is critical for many applications of intelligent systems. For such object detection task, accuracy and computational efficiency are two important...
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The ability to identify objects of interest from digital visual signals is critical for many applications of intelligent systems. For such object detection task, accuracy and computational efficiency are two important aspects, especially for applications with real-time requirement. In this paper, we study shuttlecock detection problem of a badminton robot, which is very challenging since the shuttlecock often moves fast in complex contexts, and must be detected precisely in real time so that the robot can plan and execute its following movements. To this end, we propose two novel variants of Tiny YOLOv2, a well-known deep learning based detector. We first modify the loss function to adaptively improve the detection speed for small objects such as shuttlecock. We then modify the architecture of Tiny YOLOv2 to retain more semantic information of small objects, so as to further improve the performance. Experimental results show that the proposed networks can achieve high detection accuracy with the fastest speed, compared with state-of-the-art deep detectors such as Faster R-CNN, SSD, Tiny YOLOv2, and YOLOv3. Our methods could be potentially applied to other tasks of detecting high-speed small objects.
The present work aims to promote the development of intelligent image processing technology for badminton robots and optimize the application effect of badminton robots in national fitness. Firstly, the problems and c...
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The present work aims to promote the development of intelligent image processing technology for badminton robots and optimize the application effect of badminton robots in national fitness. Firstly, the problems and common needs of the badminton robot currently in use are investigated. Secondly, a shuttlecock aerodynamic model is established to simulate the effects of air resistance and gravity on the aerial flight of shuttlecock. Besides, the convolution neural network (CNN) is combined with traditional image processing technology to denoise and recognize the collected shuttlecock images. Finally, the badminton robot vision system is constructed and its performance is tested. The results demonstrate that the image denoising method based on CNN and the traditional image processing method can effectively process and denoise the captured moving image. Under the noise level of sigma = 25, the peak signal-to-noise ratio index of this method is better than the Gaussian Scale Model, k-Singular Value Decomposition, and Color Names methods, slightly better than that of the Multilayer Perceptron (MLP) network. In terms of the time consumed in processing the same number of pictures, the method reported here takes the least time. But when sigma > 50, the MLP method has a better denoising effect because the noise is overlarge and the features are not easy to learn. Moreover, the detection accuracy of the optimized Single Shot MultiBox Detector (SSD) method adopted here is 79.0%. This accuracy is 1.7% higher than that of the traditional SSD method, and 2.3% higher than that of Faster Region-Convolutional Neural Network based on Region Proposal Network. The optimized network structure reported here provides a certain idea for the software design of the badminton robot.
With the rapid development of computer technology, target tracking has become an indispensable technology in the field of image processing. Outline-based matching algorithms are one of the most representative methods ...
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With the rapid development of computer technology, target tracking has become an indispensable technology in the field of image processing. Outline-based matching algorithms are one of the most representative methods in the field of computer vision. The idea is to extract several characteristic vectors from the image and compares them with the characteristic vectors in the corresponding image template. The difference between the image and the template characteristic vector is calculated, and the category is determined by the minimum distance method. The badminton robot collects the depth image of the scene through the depth camera and then uses the machine vision theory to process the acquired depth image. To combine the image depth information to obtain the position of the badminton camera coordinate system in the three-dimensional space, the position of the site coordinate system is achieved. Finally, the position information of the badminton in the multi-frame images is used to predict the falling point of the badminton. The badminton positioning and the analysis of the falling point are completed. The badminton robot quickly runs to the predicted position of the badminton and completes a hitting task. To realize the high-speed continuous and smooth badminton action of the badminton robot manipulator, a new multi-objective manipulator trajectory optimization model is proposed. The experimental results show that the new trajectory optimization model can effectively reduce the energy consumption of the motor and improve the rotational efficiency, thus ensuring the response speed of the arm.
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