Extraction of visual information in basketball broadcasting video for event segmentation system

Video analysis is an essential process to segment and summarize sports videos automatically. In this paper, we propose fast and simple computer vision algorithms which can be employed to an event segmentation system for basketball broadcasting videos. In our approach, camera panning is estimated by the optical flow estimation and flow segmentation algorithms. For recognizing shot classes and clock digits, Convolutional Neural Network (CNN) is used. By the experiment, it is observed that our algorithms operate in real time and are accurate to be adapted to the event segmentation system.

[1]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Daesung Moon,et al.  Robust Multi-person Tracking for Real-Time Intelligent Video Surveillance , 2015 .

[3]  Jihong Lee,et al.  Toward Accurate Road Detection in Challenging Environments Using 3D Point Clouds , 2015 .

[4]  Thomas Brox,et al.  High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Gyeong-June Hahm,et al.  Event-based sport video segmentation using multimodal analysis , 2016, 2016 International Conference on Information and Communication Technology Convergence (ICTC).

[7]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[8]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[9]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[10]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[11]  Jong Taek Lee,et al.  Real‐Time License Plate Detection in High‐Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns , 2015 .

[12]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .