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Guangyu Zhu, Changsheng Xu Qingming Huang, Wen Gao Liyuan Xing

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Player Action Recognition in Broadcast Tennis Video with Applications to Semantic Analysis of Sport Game. Guangyu Zhu, Changsheng Xu Qingming Huang, Wen Gao Liyuan Xing. Outline. Introduction Framework Overview Player Action Recognition Video Analysis Experimental Results. Introduction.

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slide1

Player Action Recognition in Broadcast Tennis Video with Applications to Semantic Analysis of Sport Game

Guangyu Zhu, Changsheng Xu Qingming Huang, Wen Gao

Liyuan Xing

outline
Outline
  • Introduction
  • Framework Overview
  • Player Action Recognition
  • Video Analysis
  • Experimental Results
introduction
Introduction
  • Semantic gap
    • between user semantics and low-level feature
    • Object in sports video can consider as an effective mid-level representation
  • Action Recognition
    • Far-view
    • Foreside-swing backside-Swing
introduction1
Introduction
  • Multimodal Framework
    • Action recognition method based on motion analysis
    • High-level analysis
      • Video Indexing
      • Highlight ranking
      • Tactic analysis
framework overview
Framework Overview
  • Sports video database
  • Low-level analysis
  • Middle-level analysis
  • Fusion scheme
  • High-level analysis
low level analysis
Low-level Analysis
  • Dominant color-based algorithm in [16] was used to identify all the in-play shots
player action recognition
Player Action Recognition
  • Related Work
    • Shah[8], Gavrila[9] recognition with close-up views
    • Motion representation
      • Motion history/energy image [12]
      • Spatial arrangement of moving points [13]
      • Several Constraints
    • Efroes[11]
      • Motion descriptor in a spatio-temporal volume
      • NNC similarity measure
    • Miyamori[14][15]
      • Base on silhouette transition
      • Appearance feature is not preserved across videos
player tracking and stabilization
Player Tracking and Stabilization
  • Player Tracking
    • Initial position: detection algo. in [16]
    • SVR particle filter [24]
  • Player region centroid
optical flow computation
Optical Flow Computation
  • Background subtraction
optical flow computation1
Optical Flow Computation
  • Noise elimination
local motion representation
Local Motion Representation
  • S-OFHs
    • slice based optical flow histogram
  • The prob. of bin(u)
  • The prob. of bin(u) in slice
local motion representation1
Local Motion Representation
  • Two slice of the figure is used
  • Horizontal and vertical optical field is used
action classification
Action Classification
  • Using SVM
  • The concatenation of four S-OFHs is fed as feature vector
  • Audio keywords
    • Silence, hitting ball, applause
action classification1
Action Classification
  • Action clip window is set to 25 frames
  • Voting Strategy
video analysis
Video Analysis
  • Fusion of mid-level features
  • Action Based Tennis Video Indexing
  • Highlights Ranking and Browsing
  • Tactics Analysis and Statistics
video indexing
Video Indexing
  • Based on action recognition and domain knowledge
highlights ranking
Highlights Ranking
  • Player action recognition
  • Real-world trajectory computation
highlights ranking1
Highlights Ranking
  • Affective Features(4 for this paper)
  • Features on action
    • Swing Switching Rate
highlights ranking2
Highlights Ranking
  • Features on trajectory
    • Speed of Player (SOP)
    • Maximum Covered Court
      • The rectangle shaped with left most, rightmost, topmost, and bottommost points
    • Direction Switching Rate
highlights ranking3
Highlights Ranking
  • The feature vector comprised of four affective features is fed into the ranking model
  • Support vector regression
  • User defined threshold
experimental results
Experimental Results
  • Action Recognition (6 seq, 194 clips)
experimental results1
Experimental Results
  • Video Indexing
experimental results2
Experimental Results
  • Highlights ranking
future work
Future Work
  • More effective slice partition
  • Involve more semantic action
    • Ex. Overhead-swing
  • Action recognition apply to more applications such as 3-D scene reconstruction
  • Include the ranking accuracy by combining audio features
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