Player Action Recognition in
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Guangyu Zhu, Changsheng Xu Qingming Huang, Wen Gao Liyuan Xing PowerPoint PPT Presentation


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

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Guangyu zhu changsheng xu qingming huang wen gao liyuan xing

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


Framework overview1

Framework Overview


Framework overview2

Framework Overview


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 action recognition1

Player Action Recognition


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


Tactics analysis and statictics

Tactics Analysis and Statictics


Experimental results

Experimental Results

  • Action Recognition (6 seq, 194 clips)


Experimental results1

Experimental Results

  • Video Indexing


Experimental results2

Experimental Results

  • Highlights ranking


Experimental results3

Experimental Results


Experimental results4

Experimental Results


Experimental results5

Experimental Results


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


Thank you

Thank You


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