Efficient Sport Videos Classification via Convolutional Neural Network
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초록
Convolutional Neural Networks (CNNs) have shown encouraging results for image classification, recognition, and retrieval tasks. In this perspective, the sport videos classification remains an active and challenging area where CNNs are less explored. Encouraged by this, we extensively provide an empirical evaluation of CNNs on sport videos classification by creating a new dataset. In this paper, we propose a CNN based method that uses MobileNetV2 (MbNetV2) network and a rolling prediction average method for sport videos classification. The proposed method uses fine-tuned MbNetV2 to classify each frame in the video and stores its prediction in a list. In rolling predition average the mean of last “K” predictions is calculated and assigned the highest probability label to the frame. We experimentally prove that our proposed method achieves the best accuracy of 97.9% on our sport dataset.
Abstract
Convolutional Neural Networks (CNNs) have shown encouraging results for image classification, recognition, and retrieval tasks. In this perspective, the sport videos classification remains an active and challenging area where CNNs are less explored. Encouraged by this, we extensively provide an empirical evaluation of CNNs on sport videos classification by creating a new dataset. In this paper, we propose a CNN based method that uses MobileNetV2 (MbNetV2) network and a rolling prediction average method for sport videos classification. The proposed method uses fine-tuned MbNetV2 to classify each frame in the video and stores its prediction in a list. In rolling predition average the mean of last “K” predictions is calculated and assigned the highest probability label to the frame. We experimentally prove that our proposed method achieves the best accuracy of 97.9% on our sport dataset.
참고문헌 (20)
- 강규창, "파티클 스웜 최적화에서의 가중치 조절에 기반한 강인한 객체 추적 알고리즘", 한국차세대컴퓨팅학회 논문지, 14(6), pp.15~29 (2018), 한국차세대컴퓨팅학회
- 임혜진, "Descriptor 조합 및 동일 병명 이미지 수량 역비율 가중치를 적용한 유사도 기반 작물 질병 검색 기술 설계 및 구현", 한국차세대컴퓨팅학회 논문지, 14(6), pp.30~43 (2018), 한국차세대컴퓨팅학회
- 최현종, "이중흐름 3차원 합성곱 신경망 구조를 이용한 효율적인 손 제스처 인식 방법", 한국차세대컴퓨팅학회 논문지, 14(6), pp.66~74 (2018), 한국차세대컴퓨팅학회
- Sajjad, M., "Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model", Sensors, 20(22), pp.6419~ (2020)
- Kapela, R., "Real-time field sports scene classification using colour and frequency space decompositions", Journal of real-time image processing, 13(4), pp.725~737 (2017)
- Song, W., "A Big-Bang Big-Crunch fuzzy logic based system for sports video scene classification", 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2016), IEEE
- Ekin, A., "Automatic soccer video analysis and summarization", IEEE Transactions on Image processing, 12(7), pp.796~807 (2003)
- Zhu, G., "Event tactic analysis based on broadcast sports video", IEEE Transactions on Multimedia, 11(1), pp.49~67 (2008)
- Xu, C., "Learning to score figure skating sport videos", IEEE Transactions on Circuits and Systems for Video Technology (2019)
- Gordon, A.S., "Automated video assessment of human performance", Proceedings of AI-ED (1995)
- Ullah, F.U.M., "Violence detection using spatiotemporal features with 3D convolutional neural network", Sensors, 19(11), pp.2472~ (2019)
- Khan, S.U., "Cover the violence: Anovel Deep-Learning-Based approach towards violencedetection in movies", Applied Sciences, 9(22), pp.4963~ (2019)
- Ul Haq, I., "Personalized movie summarization using deep cnn-assisted facial expression recognition", Complexity, 2019 (2019)
- Khan, N., "SD-Net: Understanding overcrowded scenes in real-time via an efficient dilated convolutional neural network", Journal of Real-Time Image Processing, pp.1~15 (2020)
- Sandler, M., "Mobilenetv2: Inverted residuals and linear bottlenecks", Proceedings of the IEEE conference on computer vision and pattern recognition (2018)
- Howard, A.G., "Mobilenets: Efficient convolutional neural networks for mobile vision applications", arXiv preprint arXiv:1704.04861, ~ (2017)
- He, K., "Deep residual learning for image recognition", Proceedings of the IEEE conference on computer vision and pattern recognition (2016)
- He, K., "Identity mappings in deep residual networks", European conference on computer vision (2016), Springer
- Simonyan, K., "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409.1556, ~ (2014)
- Deng, J., "Imagenet: A large-scale hierarchical image database", 2009 IEEE conference on computer vision and pattern recognition (2009), Ieee