This research aims to study the goaling recognition methods based on the intelligent analysis of real-time basketball images of the Internet of Things, so as to accelerate the development of basketball and other sports fields. First, the status of basketball-related image recognition and tracking in the field of sports is analysed, which are combined based on the background differencing and inter-frame differencing. Then, an improved image intelligent detection is proposed to identify the basketball goaling, and further analysis of the tracking algorithm related to the moving target is conducted. Finally, an improved Camshift target tracking algorithm is proposed to detect and recognize the basketball goaling. The results show that both background differencing and inter-frame differencing have large errors in basketball goaling recognition. The improved image intelligent detection algorithm in this research can fit the contour of the basketball very well, and the fitting effect of the smallest circle is ideal. From the perspective of basketball goaling trajectory tracking, it is found that the improved Camshift goal tracking algorithm has the best recognition effect on basketball goaling. In addition, the average running time of the algorithm is 209 ms, with favourable accuracy. Therefore, verified by the tests, the intelligent detection algorithm and trajectory tracking algorithm proposed in this research have high recognition accuracy for basketball goaling, and the operation efficiency is ideal, which provides experimental reference for the later intelligent development of sports fields such as basketball.

Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things

Abstract Aiming at solving the problems of low success rate and weak robustness of object tracking algorithms based on siamese network in complex scenes with occlusion, deformation, and rotation, a siamese network object tracking algorithm with attention network and adaptive loss function (SiamANAL) is proposed. Firstly, the multi-layer feature fusion module for template branch (MFFMT) and the multi-layer feature fusion module for search branch (MFFMS) are designed. The modified convolutional neural networks (CNN) are used for feature extraction through the fusion module to solve the problem of features loss caused by too deep network. Secondly, an attention network is introduced into the SiamANAL algorithm to calculate the attention of template map features and search map features, which enhances the features of object region, reduces the interference of background region, and improves the accuracy of the algorithm. Finally, an adaptive loss function combined with pairwise Gaussian loss function and cross entropy loss function is designed to increase inter-class separation and intra-class compactness of classification branches and improve the accuracy rate of classification and the success rate of regression. The effectiveness of the proposed algorithm is verified by comparing it with other popular algorithms on two popular benchmarks, the visual object tracking 2018 (VOT2018) and the object tracking benchmark 100 (OTB100). Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The success rate and precision rate of the proposed algorithm SiamANAL on OTB100 are 0.709 and 0.883, respectively. With the help of cloud computing services and data storage, the processing performance of the proposed algorithm can be further improved. 

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A cloud-oriented siamese network object tracking algorithm with attention network and adaptive loss function

A moving object tracking algorithm based on the Mean-Shift algorithm was proposed for the accurately tracking of moving target under complex environmentsThis algorithm obtained the target model through the frame difference method;then through Kalman filter to reduce the iteration times of Mean-Shift algorithm and solve occlusion;lastly through adjusting the kernel function bandwidth to change the size of tracking window in order to access the complete information of moving targetExperiment results showed that the algorithm can track the moving target stably and accurately under complex environment and robustness to occlusion without the prior information

Research of moving target tracking algorithm based on Mean-Shift

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

/pdf/continuously-adaptive-mean-shift-tracking-algorithm-based-on-4kh57rrvqi.pdf

Kailiang Zhang

Papers

Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model