Screen-Strategy Analysis in Broadcast Basketball Video using Player Tracking

Tsung-Sheng Fu , Hua-Tsung Chen , Chien-Li Chou , Wen-Jiin Tsai , and Suh-Yin LeeVisual Communications and Image Processing (VCIP), 2011 IEEE, 6-9 Nov. 2011

Outline Introduction System overview Camera calibration Player extraction and tracking Screen-strategy analysis Experimental results Conclusions

Introduction Sports video analysis

o Bring the audience efficient viewing of sports games• Highlight extraction and semantic event analysis [1, 2, 3].

o systems for tactics analysis and statistics compiling are in urgent demand [4, 5, 6]

Basketball: one of the hottest sportso Chen et al. [7] proposed a 3D ball trajectory reconstruction algorithm

which can be applied to shooting location estimation.o Chang et al. [8] introduced a wide-open warning system.

To design a system capable of telling the executed tactics explicitly

Introduction Scoring : the most important event, complicated task

o Offensive tacticso Break the defenseo Find open chance to shoot

With the tactic information, audience can learn how plays are made, and professional coaches and players can analyze the offense tendencies and strategies.

Screen： basic offensive tactico Camera calibration o Player tracking

System overview video pre-processing:

o Gathers reusable informationo Accelerates the computation

Content analysis:o Obtain their trajectories

Camera calibration Geometric mapping between world coordinates and image

coordinates.o Heavy loado Adapt the efficient court model tracking algorithm in [9]

[9] D. Farin, S. Krabbe, P. H. N. de With, W. Effelsberg, “Robust Camera Calibration for Sport Videos Using Court Models,” in Proc. SPIE, pp. 80-91, 2004.

Initial Calibration Color filtering： detect white pixels Compute structure matrix within the pixel neighborhood:

Structure can be classified by evaluating the magnitude of the two eigenvalues. • 1 >> 2 : linear structure

(b : Texture region width)

Initial Calibration Hough transform

Initial Calibration Construct an accumulator matrix

Extract the longest horizontal and vertical lines by extracting the local maxima in the accumulator matrix

vote

Initial Calibration Camera parameters : homography matrix H.

Court Model Tracking

Time consuming

Court Model Tracking Predicting the camera parameters for frame t + 1 based on

the previously computed parameters for frames t - 1 and t.

Player Detectionbackground subtraction

Computing the dominant colorwithin the court region

k-means clustering

Player Tracking Kalman filter

o With the position predicted by the Kalman filter, we select the nearest candidate as measurement.

o If a tracker is outside the court for consecutive n frames, it will be terminated

o there are some candidates not tracked =>add new trackers

Screen-strategy analysis

Screen-strategy analysis Screen Detection

o Two offensive players close to each othero At least one defender between the two offensive players standing

close to each other Screen Classification

o down-screen: screener moves to the baseline.o back-screen: the angle between the two directions is small, otherwise,

mark as front-screen• moving direction of the screenee• the direction to the basket of the screenee

Experimental results Testing videos: Beijing 2008 Olympic Games: USA vs. AUS,

ARG vs. USA, and USA vs.CHN with frame resolution of 640x352 (29.97 fps).

total of 30 video clipso Randomly select 10 clips as our training data o Remaining 20 clips are testing data.

Experimental

results

Conclusions We proposed a system that detects and

classifies screens in basketball video Our proposed system is a one-pass scheme so

that it can be applied to broadcast video.o The audience can learn offensive basketball tactics

in real-timeo Professional coaches and players can analyze the

offense tendency of the opposing team efficiently.