Tracking Ball in Soccer Game Video using Extended Kalman Filter

Najeeb, Huda Dheyauldeen; Ghani, Rana Fareed

doi:10.1109/csase48920.2020.9142058

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…In order to fully evaluate the effectiveness and advancement of our proposed method, we selected six of the currently best-performing single-object tracking methods and opensource ball-tracking methods in the comparative experimental evaluation of ball-tracking results, including UNINEXT [41], OSTrack [42], STACK [43], Deepsport [44], VolleyVision [45], and EKF [8]. UNINEXT reformulates diverse instance perception tasks into a unified object discovery and retrieval paradigm and can flexibly perceive different types of objects by simply changing the input prompts.…”

Section: Performance Comparison With Other Ball-tracking Methodsmentioning

confidence: 99%

“…In the tracking stage, existing research mainly focuses on solving the problems of motion blur and deformation caused by the fast movement of balls and trajectory incoherence caused by occlusion and out-of-drawing. Using a bounding box overlap probability measurement algorithm [4] can generate a more robust ball trajectory from tracking loss and recovery; estimating the ball's position through the extended Kalman filter [8] can better cope with sports videos. Uncertainty in ball movement: the ball's motion state is modeled based on time-varying fission filters [9], and the relative space filter is used to correct the trajectory, integrating spatiotemporal information.…”

Section: Introductionmentioning

confidence: 99%

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Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

Han,

Wang,

Wang

2024

Applied Sciences

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Most existing object tracking research focuses on pedestrians and autonomous driving while ignoring sports scenes. When general object tracking models are used for ball tracking, there are often problems, such as detection omissions due to small object sizes and trajectory loss due to occlusion. To address these challenges, we propose a ball detection and tracking model called HMMATrack based on multiscale feature enhancement and multilevel collaborative matching to improve ball-tracking results from the entire process of sampling, feature extraction, detection, and tracking. It includes a Heuristic Compound Sampling Strategy to deal with tiny sizes and imbalanced data samples; an MNet-based detection module to improve the ball detection accuracy; and a multilevel cooperative matching and automatic trajectory correction tracking algorithm that can quickly and accurately correct the ball’s trajectory. We also hand-annotated SportsTrack, a ball-tracking dataset containing soccer, basketball, and volleyball scenes. Extensive experiments are conducted on the SportsTrack, demonstrating that our proposed HMMATrack model outperforms other representative state-of-the-art models in ball detection and tracking.

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Section: Performance Comparison With Other Ball-tracking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

Han,

Wang,

Wang

2024

Applied Sciences

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“…In order to meet these challenges, a lot of work had already been carried out. Most of the work is in sports domain such as soccer [ 16 , 17 ], cricket [ 18 , 19 ], basketball [ 7 , 20 ], tennis [ 21 ], and ping-pong playing robots [ 22 , 23 , 24 , 25 ]. Some work is also carried out for catching robots such as the work in [ 26 ] for a ball catching robot where a ball 8.5 cm in diameter was wrapped in retro-reflective foil and its flight trajectory was observed through stereo triangulation by two cameras mounted as the eyes of a catching humanoid robot.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Qadeer

Shah

Sharif

et al. 2022

Sensors

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Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities. Such an approach requires intelligent tracking and prediction of the final 3D catching position of thrown objects, while observing their initial flight trajectory in real-time, by catching robot in order to grasp them accurately. Due to non-deterministic nature of such mechanically thrown objects’ flight, accurate prediction of their complete trajectory is only possible if we accurately observe initial trajectory as well as intelligently predict remaining trajectory. The thrown objects in industry can be of any shape but detecting and accurately predicting interception positions of any shape object is an extremely challenging problem that needs to be solved step by step. In this research work, we only considered spherical shape objects as their3D central position can be easily determined. Our work comprised of development of a 3D simulated environment which enabled us to throw object of any mass, diameter, or surface air friction properties in a controlled internal logistics environment. It also enabled us to throw object with any initial velocity and observe its trajectory by placing a simulated pinhole camera at any place within 3D vicinity of internal logistics. We also employed multi-view geometry among simulated cameras in order to observe trajectories more accurately. Hence, it provided us an ample opportunity of precise experimentation in order to create enormous dataset of thrown object trajectories to train an encoder-decoder bidirectional LSTM deep neural network. The trained neural network has given the best results for accurately predicting trajectory of thrown objects in real time.

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“…A deep convolutional neural network for image segmentation is used for detection, and a Kalman filter-based approach is used for tracking. In [15], an extended Kalman filter is used after the ball detection stage. Naidoo and Tapamo [16] propose another similar two-stage approach that contains soccer ball detection based on coarse analysis and filtering.…”

Section: Related Workmentioning

confidence: 99%

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

AMİRLİ

Alemdar

2022

Academic Platform Journal of Engineering and Smart Systems

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Tracking the ball location is essential for automated game analysis in complex ball-centered team sports such as football. However, it has always been a challenge for image processing-based techniques because the players and other factors often occlude the view of the ball. This study proposes an automated machine learning-based method for predicting the ball location from players' behavior on the pitch. The model has been built by processing spatial information of players acquired from optical tracking data. Optical tracking data include samples from 300 matches of the 2017-2018 season of the Turkish Football Federation's Super League. We use neural networks to predict the ball location in 2D axes. The average coefficient of determination of the ball tracking model on the test set both for the x-axis and the y-axis is accordingly 79% and 92%, where the mean absolute error is 7.56 meters for the x-axis and 5.01 meters for the y-axis.

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Tracking Ball in Soccer Game Video using Extended Kalman Filter

Cited by 6 publications

References 6 publications

Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0

Prediction of the Ball Location on the 2D Plane in Football Using Optical Tracking Data

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