Experimental Result of Scene Change Detection and Shot Classification

We use two basketball videos of HBL (High-school Basketball League) to test the scene change detection and shot classification algorithm. The first video is a 15 minutes long basketball video which contains 96 shots ( 37 Close-up view shots, 27 Medium view shots, and 32 Full-court view shots), and the other is 10 minutes long and contains 71 shots ( 26 Close-up view shots, 24 Medium view shots, and 21 Full-court view shots). Table. 1 shows the classification results.!

From Table. 1, the accuracy of our shot classification algorithm is about 95.2%

(the number of correctly classified shots divided by the number of total shots). The miss and false situation may be caused by the angle of view. For instance, if a real full court view shot contains large portion of spectators, the ratio of the court dominant color will be lower, which results in wrong classification.

Close up Medium Full court

Sequence 1 Sequence 2 Sequence 1 Sequence 2 Sequence 1 Sequence 2

Ground Truth 37 26 27 24 32 21

No. of Miss 1 2 2 2 0 1

No. of False 0 1 1 3 2 1

Table. 1 Shot classification results of two testing sequences. Sequence 1 is a 15 minutes basketball video containing 96 shots, and sequence 2 is a 10 minutes basketball video containing 71 shots.

4.2 Experimental Result of Tracking the Ball

Using the proposed ball candidate search and tracking methods, we can obtain the 2D trajectories from the full court view shots. Fig. 4-1 is the tracking result of a shot without camera motion, and Fig. 4-2 is the tracking result of a shot with camera motion. No matter the sport video is shot by stationary camera or not, we can obtain its possible 2D trajectories.

Fig. 4-2 The tracking result of a shot with camera motion.

4.3 Experimental Result of Camera Calibration and Shooting Position

In this section, we only use the clips without camera motion to test the camera calibration algorithm. As Fig. 4-3 shows, the location of the points for camera calibration and the backboard position can be derived from the image. Therefore, the real shooting trajectory presented by solid circles can be identified as shown in Fig.

4-4. Use the transformation relationship from 2D coordinate to 3D coordinate, we can obtain the shot position. Fig. 4-5 indicates the 3D shooting position by a red point.

Fig. 4-3 The 2D location of the points for camera calibration and the backboard position.

Fig. 4-4 The real 2D ball trajectory.

Fig. 4-5 The obtained shooting position in 3D court model.

Chapter 5

Conclusion and Future Work

Sport event detection has been proposed in previous research. However, these events only provide the audience a more efficient way to browse through sport videos.

We propose a system that can automatically detect the scene change of the basketball video and classify clips into three kinds of shots. With the full-court-view shots, we can track the ball in the videos, detect the court-line and the backboard positions, and define the transformation relationship from 2D image to 3D real-world court model.

After mapping the position of the ball from images to court model, the system concludes the possible shooting positions.

Analyzing tactics in basketball video is difficult due to the variation of view angle, the complexity of background and the intricacy of court lines. Our ball tracking method can be used for any full court view shot no matter whether there is camera motion or not. However, the camera calibration algorithm can only be applied for clips without camera motion.

Since the camera is not fixed, the result of shooting positions might not be accurate enough. The future work can be concentrated on videos shot by stationary camera so that the system will be more reliable. Tracking players in the video is difficult because occlusion occurs when players get close. If we can propose a more effective and efficient tracking algorithm, we could gather more statistics to analyze the behavior of the players in the games. Furthermore, we can conclude useful knowledge such as the defense rank and the offense tactics for professional basketball players and coaches who need more detailed information of the game.

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