Haojie Li Jinhui Tang Si Wu Yongdong Zhang Shouxun Lin Automatic Detection and Analysis of Player Action in Moving Background Sports Video Sequences IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 20, NO. 3, MARCH 2010

 Introduction  Global motion estimation  Player body shape segmentation  Analysis of action  Experimental results OUTLINE

 Presents a system for automatically detecting and analyzing complex player actions in moving background sports video sequences  Providing kinematic measurements for coach assistance and performance improvement  Video-based approach : low cost, no interference to the performance of players, can analyze the rich archived video clips INTRODUCTION

 Block diagram of the proposed system INTRODUCTION Video Sequence Global motion estimation Action clips detection Action recognition Player shape segmentation Visual analysis Kinematic analysis Highlights library 1. The detected highlights are stored into library as video summaries for user’s quick browsing 2. Action recognize using CHMM ( continuous hidden markov models ) Action-based video indexing Kinematic parameters

GLOBAL MOTION ESTIMATION Global motion parameter

GLOBAL MOTION ESTIMATION

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GLOBAL MOTION ESTIMATION Global motion vectors between two frames Outlier filtering Aligned image using estimated GME parameters Difference image between (b) (e) Background is accurately aligned

PLAYER BODY SHAPE SEGMENTATION … … Consecutive 2L-1 frames d1d2 ∩

 Results of Algo1 PLAYER BODY SHAPE SEGMENTATION Problem: Work well only when object has apparent motion Reason: Doesn’t consider the object motion between frames

PLAYER BODY SHAPE SEGMENTATION Only selected key-frames are used to construct background … … Consecutive 2L-1 frames … … kf 1 kf L1 Kf -1 kf L2 L1L1 L2L2

 Results of Algo2 PLAYER BODY SHAPE SEGMENTATION Th1 = 4 TH2 = 50 L = 11

 Kinematic analysis : we present an automatic method through 2-D articulated human body model fitting, to get the joint angles. S=( x, y, θ, θ 1, θ 2, θ 3, d ) THE ANALYSIS OF ACTION Human body model Test body shape Edge map Distance transform map [35] The initial parameter is refine by searching with annealed particle algorithm[34] Global position & rotation parameter neck, hip, knee angle

 Visual analysis  Motion Panorama  Overlay composition THE ANALYSIS OF ACTION Temporal median filtering Compare actions performed by different players or by the same player at different time No constraint that two clips should be of same scene

 Global motion estimation EXPERIMENTAL RESULTS ( Interframe transformation fidelity ) Aligned image to I k by global motion compensated on I k-1 RANSAC & LTS with refinement procedures

 Player body segmentation EXPERIMENTAL RESULTS

 Action recognition EXPERIMENTAL RESULTS

 Kinematic analysis EXPERIMENTAL RESULTS

 Visual analysis EXPERIMENTAL RESULTS

 Experiments on jump videos EXPERIMENTAL RESULTS