Soccer video shots and field areas are the necessary conditions for soccer video event detection

which plays an important role in the semantic analysis of soccer videos. A new fluctuation detection method is proposed to classify the soccer video shot to address the shortcomings of existing shot classification methods. This method adopts a sliding window to slide in the video frame image. The fluctuation times of the field pixel ratio that crosses the threshold ratio of the in-field shot are recorded

and then the shot type is judged according to the fluctuation number. A novel approach is also proposed to identify the playfield zone type by GMM (Gaussian Mixture Model)

which is simple and efficient

by applying the position relationship between the upper left corner and upper right corner. Experimental results show that the two methods exhibit significant improvement in accuracy recall rate

high detection efficiency

and can meet the real-time requirements

unlike existing classification methods. The proposed methods can address the problem of the traditional sliding window method that incorrectly identifies the large frame image of the field inclination angle. These methods can also solve the problem of the traditional field area detection method relying on the field line detection

which leads to a low accuracy rate.