Soccer Video Structure Analysis by Parallel Feature Fusion Network and Hidden-to-Observable Transferring Markov Model

Fani, Mehrnaz; Yazdi, Mehran; Clausi, David A.; Wong, Alexander

doi:10.1109/access.2017.2769140

Cited by 15 publications

(5 citation statements)

References 32 publications

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“…Occupied players in football games can be tracked with this technology, which also works well for multitarget monitoring in other team sports characterized by similar physical characteristics and unexpected movements. Fani et al [21] examine football movies' structure, which includes the use of parallel feature-fusion networks to merge local and panoramic 2 Wireless Communications and Mobile Computing characteristics to identify perspectives, and then provide an advanced Markov model to detect the playback status of shots. It is possible to identify transition effects using a threshold-based technique and Gaussian mixture models for event candidates [22].…”

Section: Related Workmentioning

confidence: 99%

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Zhong

2022

Wireless Communications and Mobile Computing

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Basketball is a sport with a wide range of complicated human movements, and the ability to accurately identify these movements is critical in both competition and training. Training athletes is largely based on the subjective observations and experiences of the coaches. Artificial intelligence and big data technologies can be used to monitor athlete training. It can also assist coaches in making decisions which significantly increase the athletic ability by detecting their movements. This research work proposed a mechanism called BSTARNet which is an action recognition method for basketball sports training. The method is based on artificial neural network (ANN), and network is trained through basketball sports big data. First, this work uses the Convolution Long Short-Term Memory (ConvLSTM) unit to extract the spatiotemporal information features of basketball sports training from videos. Second, this work establishes the Attention Long Short-Term Memory (AttLSTM) unit that combines the attention mechanism with the LSTM. The unit selectively scans each location, giving more attention to the area where the action takes place. Finally, the network framework is built by improving the ordinary encoder-decoder model. After that, the spatiotemporal information contained in the video is encoded based on the Darknet network model. In the decoding stage, the AttLSTM structure is used to replace the ordinary LSTM. These units are combined to form the BSTARNet architecture. Experiments are conducted to verify the effectiveness of the proposed method applied on action recognition in basketball sports training and achieved 89.5% mAP and 95.4% accuracy.

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Section: Related Workmentioning

confidence: 99%

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Zhong

2022

Wireless Communications and Mobile Computing

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“…In the last experiment, we compared the performance of our method against the existing shot classification methods [24][25][26] and [30][31][32][33] for sports videos.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

“…Gabor filters were applied and trained the SVM to classify the scene into different shots. Fani et al [30] proposed a deep learning fused features-based framework to classify the shot types using the camera zoom and out-field information. The soft-max and fussing Bayesian layers were used to classify the shots into long, medium, close-up, and out-field shots.…”

Section: Performance Comparison With Existing Methodsmentioning

confidence: 99%

Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network

et al. 2019

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Broadcasters produce enormous numbers of sport videos in cyberspace due to massive viewership and commercial benefits. Manual processing of such content for selecting the important game segments is a laborious activity; therefore, automatic video content analysis techniques are required to effectively handle the huge sports video repositories. The sports video content analysis techniques consider the shot classification as a fundamental step to enhance the probability of achieving better accuracy for various important tasks, i.e., video summarization, key-events selection, and to suppress the misclassification rates. Therefore, in this research work, we propose an effective shot classification method based on AlexNet Convolutional Neural Networks (AlexNet CNN) for field sports videos. The proposed method has an eight-layered network that consists of five convolutional layers and three fully connected layers to classify the shots into long, medium, close-up, and out-of-the-field shots. Through the response normalization and the dropout layers on the feature maps we boosted the overall training and validation performance evaluated over a diverse dataset of cricket and soccer videos. In comparison to Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbors (KNN), and standard Convolution Neural Network (CNN), our model achieves the maximum accuracy of 94.07%. Performance comparison against baseline state-of-the-art shot classification approaches are also conducted to prove the superiority of the proposed approach.

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“…They integrated multiple person-centered features with LSTM cell output over temporal sequences. Further, Fani et al [38] introduced a parallel feature fusion (PFF) network for automatic event detection and classification in soccer broadcast videos. The PFF combined local as well as full scene features for zoom in and zoom out scene classification.…”

Section: Deep Learning-based Event Recognition Approachesmentioning

confidence: 99%

AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

Muhammad

Ullah

Obaidat

et al. 2023

IEEE Internet Things J.

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The salient events recognition of soccer matches in next-generation Internet of things (Nx-IoT) environment aims to analyze the performance of players/teams by the sports analytics and managerial staff. The embedded Nx-IoT devices carried by the soccer players during the match capture and transmit data to an Artificial Intelligence (AI)-assisted computing platform. The interconnectivity of data acquisition devices with an AI-assisted computing platform in the Nx-IoT environment will not only allow the spectators to track the formation of their favorite players during a soccer match but will also enable the managerial staff to evaluate the players' performance in the soccer match as well as in practice sessions. This Nx-IoT-enabled salient event detection feature can be provided to spectators and sports' managerial staff as a financial technology (FinTech) service. In this paper, we propose an efficient deep learning-based framework for multiperson salient soccer events recognition in IoT-enabled FinTech. The proposed framework performs event recognition in three steps: Firstly, image frames are extracted from video streams and resized in the preprocessing step to match the input of the deep network. Secondly, frame-level discriminative features are extracted using a pre-trained convolutional neural network (CNN) architecture. Thirdly, we employ a multi-layer long short-term memory (MLSTM) network to recognize high-level events in soccer videos by exploiting the sequential relation between adjacent frames. Moreover, we introduce a new soccer video events (SVE) dataset containing videos of six salient events of soccer game. To provide a strong baseline, we evaluate our newly created SVE dataset using different traditional machine learning and deep learning algorithms. We also perform event recognition on untrimmed soccer videos using our proposed framework and compare the results with state-of-the-art methods. The obtained results validate the suitability of our proposed framework for salient events recognition in Nx-IoT environments.

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Soccer Video Structure Analysis by Parallel Feature Fusion Network and Hidden-to-Observable Transferring Markov Model

Cited by 15 publications

References 32 publications

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network

AI-Driven Salient Soccer Events Recognition Framework for Next-Generation IoT-Enabled Environments

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