论文标题
基于卷积神经网络和卡尔曼滤波器的有效高尔夫球检测和跟踪
Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter
论文作者
论文摘要
本文重点介绍了在线高尔夫球检测和图像序列跟踪的问题。通过利用基于卷积的神经网络（CNN）对象检测和基于卡尔曼滤波器的预测，提出了一种有效的实时方法。实现并评估了五个基于深度学习的对象检测网络，以进行球检测，包括Yolo V3及其小型版本Yolo V4，更快的R-CNN，SSD和Repinedet。检测是在小图像贴片上进行的，而不是整个图像，以提高小球检测的性能。在跟踪阶段，使用离散的卡尔曼滤波器来预测球的位置，并根据预测裁剪了一个小图像贴片。然后，将对象检测器用于完善球的位置并更新Kalman滤波器的参数。为了训练检测模型并测试跟踪算法，创建和注释了高尔夫球数据集的集合。进行了广泛的比较实验，以证明所提出的方案的有效性和出色的跟踪性能。
This paper focuses on the problem of online golf ball detection and tracking from image sequences. An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. Five classical deep learning-based object detection networks are implemented and evaluated for ball detection, including YOLO v3 and its tiny version, YOLO v4, Faster R-CNN, SSD, and RefineDet. The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection. At the tracking stage, a discrete Kalman filter is employed to predict the location of the ball and a small image patch is cropped based on the prediction. Then, the object detector is utilized to refine the location of the ball and update the parameters of Kalman filter. In order to train the detection models and test the tracking algorithm, a collection of golf ball dataset is created and annotated. Extensive comparative experiments are performed to demonstrate the effectiveness and superior tracking performance of the proposed scheme.
