Hockey activity recognition using pre-trained deep learning model

Rangasamy, K S; As’ari, Muhammad Amir; Rahmad, Nur Azmina; Ghazali, Nurul Fathiah

doi:10.1016/j.icte.2020.04.013

Cited by 35 publications

(15 citation statements)

References 10 publications

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“…The present study has demonstrated that through the proposed pipeline, a better classification accuracy could be achieved as compared to the conventional means reported in the literature, particularly with regards to the classification of skateboarding tricks. The encouraging results reported suggests that the proposed pipeline could be beneficial in providing an objective-based judgment in The findings of the present investigation are in agreement with other studies that have employed such a technique in different applications, for instance, Lee, Yoon and Cho (2017) , Rangasamy et al (2020) as well as Mahendra Kumar et al (2021) . Nonetheless, it is worth noting that the efficacy of the pipelines is highly dependent on the dataset utilized, and the performance may vary.…”

Section: Resultssupporting

confidence: 91%

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The classification of skateboarding tricks via transfer learning pipelines

Abdullah

Ibrahim

Shapiee

et al. 2021

PeerJ Computer Science

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This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

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Section: Resultssupporting

confidence: 91%

“…Conversely, Rangasamy et al (2020) proposed the employment of the Transfer Learning paradigm for hockey activity recognition. The authors employed a pre-trained CNN, specifically VGG16, to extract features from four main hockey activities, namely free hit, goal, penalty corner and long corner, respectively.…”

Section: Introductionmentioning

confidence: 99%

The classification of skateboarding tricks via transfer learning pipelines

Abdullah

Ibrahim

Shapiee

et al. 2021

PeerJ Computer Science

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show abstract

“…The sensor placement in the wrist and upper arm had a weighted accuracy of 93% and 96%, respectively, which was marginally lower than the average accuracy of 98%. Rangasamy et al [ 23 ] proposed a model using the VGG16 pretrained network to classify 4 hockey activities, namely, free hit, goal, penalty corner, and long corner. The highest accuracy of 98% was reached after running the model for 300 epochs.…”

Section: Related Workmentioning

confidence: 99%

CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit

Sen

Deb

Dhar

et al. 2021

Sensors

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Recognizing the sport of cricket on the basis of different batting shots can be a significant part of context-based advertisement to users watching cricket, generating sensor-based commentary systems and coaching assistants. Due to the similarity between different batting shots, manual feature extraction from video frames is tedious. This paper proposes a hybrid deep-neural-network architecture for classifying 10 different cricket batting shots from offline videos. We composed a novel dataset, CricShot10, comprising uneven lengths of batting shots and unpredictable illumination conditions. Impelled by the enormous success of deep-learning models, we utilized a convolutional neural network (CNN) for automatic feature extraction, and a gated recurrent unit (GRU) to deal with long temporal dependency. Initially, conventional CNN and dilated CNN-based architectures were developed. Following that, different transfer-learning models were investigated—namely, VGG16, InceptionV3, Xception, and DenseNet169—which freeze all the layers. Experiment results demonstrated that the VGG16–GRU model outperformed the other models by attaining 86% accuracy. We further explored VGG16 and two models were developed, one by freezing all but the final 4 VGG16 layers, and another by freezing all but the final 8 VGG16 layers. On our CricShot10 dataset, these two models were 93% accurate. These results verify the effectiveness of our proposed architecture compared with other methods in terms of accuracy.

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“…In detail, we are based on the dual-stream architecture introduced by [34] on the VGG-16 network. Here, we consider only appearance streams but discuss different ways of combining appearance and motion streams with our aggregation in Section 3.1.…”

Section: Aggregation Layermentioning

confidence: 99%

Semantic Extraction of Basketball Game Video Combining Domain Knowledge and In-Depth Features

Zhao

2021

Scientific Programming

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The team sports game video features complex background, fast target movement, and mutual occlusion between targets, which poses great challenges to multiperson collaborative video analysis. This paper proposes a video semantic extraction method that integrates domain knowledge and in-depth features, which can be applied to the analysis of a multiperson collaborative basketball game video, where the semantic event is modeled as an adversarial relationship between two teams of players. We first designed a scheme that combines a dual-stream network and learnable spatiotemporal feature aggregation, which can be used for end-to-end training of video semantic extraction to bridge the gap between low-level features and high-level semantic events. Then, an algorithm based on the knowledge from different video sources is proposed to extract the action semantics. The algorithm gathers local convolutional features in the entire space-time range, which can be used to track the ball/shooter/hoop to realize automatic semantic extraction of basketball game videos. Experiments show that the scheme proposed in this paper can effectively identify the four categories of short, medium, long, free throw, and scoring events and the semantics of athletes’ actions based on the video footage of the basketball game.

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Hockey activity recognition using pre-trained deep learning model

Cited by 35 publications

References 10 publications

The classification of skateboarding tricks via transfer learning pipelines

The classification of skateboarding tricks via transfer learning pipelines

CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit

Semantic Extraction of Basketball Game Video Combining Domain Knowledge and In-Depth Features

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