Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter

• URL: http://arxiv.org/abs/2012.09393v2
• Date: Wed, 21 Apr 2021 18:06:10 GMT
• Title: Efficient Golf Ball Detection and Tracking Based on Convolutional Neural Networks and Kalman Filter
• Authors: Tianxiao Zhang, Xiaohan Zhang, Yiju Yang, Zongbo Wang, Guanghui Wang
• Abstract summary: An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. The detection is performed on small image patches instead of the entire image to increase the performance of small ball detection. In order to train the detection models and test the tracking algorithm, a collection of golf ball dataset is created and annotated.
• Score: 15.899498333913975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Rethinking Voxelization and Classification for 3D Object Detection [68.8204255655161]
  The main challenge in 3D object detection from LiDAR point clouds is achieving real-time performance without affecting the reliability of the network. We present a solution to improve network inference speed and precision at the same time by implementing a fast dynamic voxelizer. In addition, we propose a lightweight detection sub-head model for classifying predicted objects and filter out false detected objects.
  arXiv  Detail & Related papers  (2023-01-10T16:22:04Z)
• Minkowski Tracker: A Sparse Spatio-Temporal R-CNN for Joint Object Detection and Tracking [53.64390261936975]
  We present Minkowski Tracker, a sparse-temporal R-CNN that jointly solves object detection and tracking problems. Inspired by region-based CNN (R-CNN), we propose to track motion as a second stage of the object detector R-CNN. We show in large-scale experiments that the overall performance gain of our method is due to four factors.
  arXiv  Detail & Related papers  (2022-08-22T04:47:40Z)
• New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis [0.1503974529275767]
  This paper proposes a robust feature extraction method for SAR image target classification by adaptively fusing effective features from different CNN layers. Experiments on the MSTAR dataset demonstrate that the proposed method outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-28T18:10:26Z)
• HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery [1.2249546377051437]
  We present our deep neural network-based combined object detection and tracking model, namely, Heat Map Network (HM-Net) HM-Net is significantly faster than state-of-the-art frame differencing and background subtraction-based methods. It outperforms state-of-the-art WAMI moving object detection and tracking methods on WPAFB dataset.
  arXiv  Detail & Related papers  (2021-10-19T11:56:30Z)
• MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking [72.65494220685525]
  We propose a new dynamic modality-aware filter generation module (named MFGNet) to boost the message communication between visible and thermal data. We generate dynamic modality-aware filters with two independent networks. The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively. To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search by exploiting a new direction-aware target-driven attention mechanism.
  arXiv  Detail & Related papers  (2021-07-22T03:10:51Z)
• DAAIN: Detection of Anomalous and Adversarial Input using Normalizing Flows [52.31831255787147]
  We introduce a novel technique, DAAIN, to detect out-of-distribution (OOD) inputs and adversarial attacks (AA) Our approach monitors the inner workings of a neural network and learns a density estimator of the activation distribution. Our model can be trained on a single GPU making it compute efficient and deployable without requiring specialized accelerators.
  arXiv  Detail & Related papers  (2021-05-30T22:07:13Z)
• Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection [4.683939045230724]
  Current object detectors are difficult to strike a balance between high accuracy and fast inference speed. We present an efficient and robust fully convolutional neural network model to perform robotic grasping pose estimation. The network is an order of magnitude smaller than other excellent algorithms.
  arXiv  Detail & Related papers  (2021-01-25T16:36:53Z)
• Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection [0.0]
  We propose an attention based feature aggregation technique in graph neural network (GNN) for detecting objects in LiDAR scan. In each layer of the GNN, apart from the linear transformation which maps the per node input features to the corresponding higher level features, a per node masked attention is also performed. The experiments on KITTI dataset show that our method yields comparable results for 3D object detection.
  arXiv  Detail & Related papers  (2020-09-17T12:56:17Z)
• Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible Noises [87.53808756910452]
  A cooling-shrinking attack method is proposed to deceive state-of-the-art SiameseRPN-based trackers. Our method has good transferability and is able to deceive other top-performance trackers such as DaSiamRPN, DaSiamRPN-UpdateNet, and DiMP.
  arXiv  Detail & Related papers  (2020-03-21T07:13:40Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.