The intelligent football players’ motion recognition system based on convolutional neural network and big data

Wang, Xin; Guo, Yingqing

doi:10.1016/j.heliyon.2023.e22316

Cited by 1 publication

(1 citation statement)

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“…The identification of occurrences that vary from anticipated behaviour is known as anomaly detection in videos, and it is a critical task in both video analytics and video surveillance [1]. However, for the following reasons, video anomaly identification is a very difficult task: First of all, genuine video data is intricate, and certain anomalous data points may be located on the edge of regular regions.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

P. Naaraju

2024

jes

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Motion detection in video streams is important for many applications, such as autonomous navigation, human-computer interaction, and spying. Conventional techniques, which depend on backdrop removal or frame differencing, frequently break down when dealing with intricate motion patterns and occlusions. Current techniques struggle to handle occlusions and correctly capture complex motion patterns. Furthermore, these techniques could be computationally expensive, especially when dealing with big amounts of video data. An integrated strategy combining optical flow estimation with 3D convolutional neural network (3D-CNN) architectures is presented to address these limitations and boost motion detection systems' accuracy and efficiency. The suggested technique is unusual as it combines 3D CNNs with optical flow estimates. Motion vectors representing dynamic scene changes are produced by optical flow estimates, and spatiotemporal characteristics are extracted by 3D CNNs processing together with optical flow information. By using the complementing capabilities of both approaches, the method performs better in motion detection applications such as object tracking, action recognition, and anomaly detection in video streams. The efficiency of the strategy is assessed by experiments carried out on benchmark datasets. The results show that it is more accurate, resistant against occlusions, and computationally efficient than existing approaches. The suggested approach offers a viable way to improve motion detection capabilities for a range of practical uses. The results show a 98% improvement in processing efficiency and motion detection accuracy when compared to baseline methods. More research and advancement in this area are made possible by the attainable way that MATLAB's suggested approach offers to enhance motion detection skills in a variety of real-world applications.

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Section: Introductionmentioning

confidence: 99%

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

P. Naaraju

2024

jes

View full text Add to dashboard Cite

show abstract

The intelligent football players’ motion recognition system based on convolutional neural network and big data

Cited by 1 publication

References 38 publications

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

Advancements in Motion Detection Within Video Streams Through the Integration of Optical Flow Estimation and 3D-Convolutional Neural Network Architectures

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