Deep Learning-Based Football Player Detection in Videos

The main task of football video analysis is to detect and track players. In this work, we propose a deep convolutional neural network-based football video analysis algorithm. This algorithm aims to detect the football player in real time. First, five convolution blocks were used to extract a feature map of football players with different spatial resolution. Then, features from different levels are combined together with weighted parameters to improve detection accuracy and adapt the model to input images with various resolutions and qualities. Moreover, this algorithm can be extended to a framework for detecting players in any other sports. The experimental results assure the effectiveness of our algorithm.

Automated summary

This study proposes a deep convolutional neural network-based football video analysis algorithm for detecting and tracking football players in real time. The algorithm utilizes five convolution blocks to extract feature maps of players with different spatial resolutions. By combining features from different levels and using weighted parameters, it improves detection accuracy and adapts to input images of varying resolutions and qualities. The experimental results confirm the effectiveness of the algorithm.