Sports video analysis has received much attention in recent years and is a challenging research direction in the field of computer vision.A novel prediction method for the behavior of ball carriers in basketball matches is proposed in this paper.Aiming at the cluttered background

fast motion of the sportsmen

and the low resolution of the head images in basketball match videos

we propose the adoption of a covariance descriptor to fuse multiple visual features of the head region

which can be represented as Riemannian Manifolds.Then we map the covariance descriptor to the tangent space and complete the head pose classification through the trained multiclass Logitboosts directly in this space for determining the range of vision of the ball carrier.According to the distribution of all the sportsmen within the range of vision of the ball carrier

we predict the behavior of him—shooting

passing

and dribbling

through sportsmen information based on artificial potential field (APF).Finally

the tests on the basketball match videos verify the effectiveness of our method.