Comparison of Player Tracking-by-Detection Algorithms in Football Videos
Abstract: In recent years, increasing demands on sports analytics have triggered growing research interest in automatic player tracking-by-detection approaches. Two prominent branches in this area are Convolutional Neural Network (CNN)-based visual object detectors and histogram-based detectors. In this thesis, we focus on a particular sub-domain: player tracking by detection in broadcast football games. To tackle challenges in this domain, such as motion blur and varied image quality, two different systems are proposed based on histogram and CNN respectively. With the help of transfer learning, the CNN-based system is fine-tuned from a pre-trained Tiny-You Only Look Once (YOLO)-V2 model. Experiments are conducted to evaluate the CNN-based system against the histogram-based system and off-the-shelf benchmarks, such as Faster Region-based convolutional Neural Networks (R-CNN). Results indicate that the CNN-based system outperforms the others in terms of mean Intersection Over Union (IOU) and Mean Average Precision (mAP). Furthermore, we combine the CNN-based system with a histogram-based post-processor to take advantage of the player's visual appearance characteristic. The combined system is evaluated against the pure CNN-based system and CNN-Simple Online and Realtime Tracking (SORT) system. Results reveal that the combined system manages to achieve better detection accuracy in terms of F1 and ITP scores.
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