Object Detection and Tracking Based on Trajectory in Broadcast Tennis Video

Archana, M.; Geetha, M. Kalaiselvi

doi:10.1016/j.procs.2015.08.060

Cited by 27 publications

(12 citation statements)

References 7 publications

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“…"Roller skating is also called roller skating; it is an ice sport in the land auxiliary training process gradually evolved in the formation of sport." With the continuous development and growth of sports, roller skating has also been flourishing, and the new projects of modern roller skating have emerged and diversified, developing different forms of sports such as speed skating, roller skating ball, extreme roller skating, and figure skating, which are popular among people [1]. e International Roller Derby Federation was officially admitted as a member of the International Olympic Committee (IOC) at the IOC Session held in Prague, Czechoslovakia, in 2007 [2].…”

Section: Introductionmentioning

confidence: 99%

Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

Duan¹,

Meng²,

Ma³

et al. 2021

Scientific Programming

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With the continuous progress of science and technology, the sport of roller skating has developed rapidly and the technical level of the game has become higher and higher. Its sport performance has been rapidly improved. However, China’s roller skating is relatively late, and there is still a certain gap compared with many Western developed countries. In order to improve the performance of China’s roller skating, this study takes the representative Chinese and foreign excellent speed skaters as the research object and compares the sprinting technology of Chinese and foreign excellent speed skaters by using image measurement and image analysis to obtain the kinematic parameters and data of the athletes’ sprinting technology in the competition state. In view of the problem that the current video target tracking algorithm is easy to follow multiple targets, a video multiobject detection and tracking algorithm with improved tracking learning detection (TLD) is studied with the skater in the video as the research object. For the lost target, the prediction function of Kalman filter algorithm is used to track the trajectory of the typical target in the video, and the trajectory tracked by Kalman filter algorithm is used to compensate the lost part of TLD algorithm, so as to obtain the complete trajectory of the typical target in the video to improve the accuracy of video multiobject tracking. Since the existing trajectory prediction algorithms have the limitation of poor accuracy, a social-long short-term memory (Social-LSTM) network-based video typical target trajectory prediction algorithm is proposed to predict the trajectory sequences of typical targets to be detected by incorporating the contextual environment information and the interaction relationship between multiple target trajectories into the Social-LSTM network. The simulation results show that the proposed trajectory prediction algorithm outperforms the traditional LSTM algorithm, Hidden Markov Model Algorithm, and Hybrid Gaussian model algorithm, which is helpful to improve the accuracy of video roller skater target trajectory prediction, and the tracking success rate is 0.98.

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Section: Introductionmentioning

confidence: 99%

Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

Duan¹,

Meng²,

Ma³

et al. 2021

Scientific Programming

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“…Estimation algorithms are the basis of tracking algorithms not only for large objects but also for small ones. These algorithms depend on the signal acquisition methods and are used for video systems [7][8][9][10][11][12].…”

Section: Related Workmentioning

confidence: 99%

Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

Matczak

Mazurek

2021

Remote Sensing

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Background estimation algorithms are important in UAV (Unmanned Aerial Vehicle) vision tracking systems. Incorrect selection of an algorithm and its parameters leads to false detections that must be filtered by the tracking algorithm of objects, even if there is only one UAV within the visibility range. This paper shows that, with the use of genetic optimization, it is possible to select an algorithm and its parameters automatically. Background estimation algorithms (CNT (CouNT), GMG (Godbehere-Matsukawa-Goldberg), GSOC (Google Summer of Code 2017), MOG (Mixture of Gaussian), KNN (K–Nearest Neighbor–based Background/Foreground Segmentation Algorithm), MOG2 (Mixture of Gaussian version 2), and MEDIAN) and the reference algorithm of thresholding were tested. Monte Carlo studies were carried out showing the advantages of the MOG2 algorithm for UAV detection. An empirical sensitivity analysis was presented that rejected the MEDIAN algorithm.

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“…To compare and evaluate the performance of TrackNet, we implement Archanas algorithm [1] which uses conventional image processing techniques to detect tennis ball. Archana's algorithm firstly smooths the image of each frame by a median filter to remove noise.…”

Section: Related Workmentioning

confidence: 99%

“…The Archana's algorithm [1], an image processing technique developed by Archana and Geetha, is implemented for comparison. The prediction details of Archana's algorithm, TrackNet Model I, and TrackNet Model II are shown in Table IV, where TP, FP, TN, and FN stand for true positive, false positive, true negative, and false negative, respectively.…”

Section: Datasetmentioning

confidence: 99%

“…Due to a relatively long shutter time of consumer or prosumer cameras, images of highspeed objects are prone to suffer from afterimage or blur issues, resulting in poor image recognition accuracy. The performance of ball tracking can be improved by pairing candidates from frame to frame according to trajectory models to find the most possible one [1]. In addition, a classical technique in image processing to improve image quality is by fusing multiple low-quality images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

Huang

Liao

Chen

et al. 2019

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Ball trajectory data are one of the most fundamental and useful information in the evaluation of players' performance and analysis of game strategies. Although vision-based object tracking techniques have been developed to analyze sport competition videos, it is still challenging to recognize and position a high-speed and tiny ball accurately. In this paper, we develop a deep learning network, called TrackNet, to track the tennis ball from broadcast videos in which the ball images are small, blurry, and sometimes with afterimage tracks or even invisible. The proposed heatmap-based deep learning network is trained to not only recognize the ball image from a single frame but also learn flying patterns from consecutive frames. TrackNet takes images with the size of 640 × 360 to generate a detection heatmap from either a single frame or several consecutive frames to position the ball and can achieve high precision even on public domain videos. The network is evaluated on the video of the men's singles final at the 2017 Summer Universiade, which is available on YouTube. The precision, recall, and F1-measure of TrackNet reach 99.7%, 97.3%, and 98.5%, respectively. To prevent overfitting, 9 additional videos are partially labeled together with a subset from the previous dataset to implement 10-fold cross validation, and the precision, recall, and F1-measure are 95.3%, 75.7%, and 84.3%, respectively. A conventional image processing algorithm is also implemented to compare with TrackNet. Our experiments indicate that TrackNet outperforms conventional method by a big margin and achieves exceptional ball tracking performance. The dataset and demo video are available at https://nol.cs.nctu.edu.tw/ndo3je6av9/.

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Object Detection and Tracking Based on Trajectory in Broadcast Tennis Video

Cited by 27 publications

References 7 publications

Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

Application Research on Roller Skater Detection, Tracking, and Trajectory Prediction Based on Video Stream

Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications

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