Goal Event Detection in Soccer Video using Fuzzy Inference System

Document Type : Research Paper



Goal is the most important event in soccer matches; thus, goal event detection is very useful for video summarization and video retrieval. In this article, we propose a new goal event detection method in the broadcast soccer videos using a fuzzy inference system. In this method, audio-visual data is processed and low-level and mid-level features are extracted to detect the goal event as a high-level concept. At the low-level processing stage, audio energy is extracted from the audio signals and 3D RGB histogram is computed for each frame. Additionally, boundaries of shots are detected as low-level features. Then, mid-level processes are accomplished. This stage contains view type recognition, logo detection, and replay boundary detection. Finally, the video is segmented to some semantic parts and a fuzzy inference system investigates the content of each semantic part to detect goal events. The main contribution of our method is presentation of expert's knowledge and heuristic rules in the form of fuzzy rules for goal event detection. This method benefits from fuzzy modeling and fuzzy inference systems while presents heuristic rules in a simple and understandable form. A soccer video data set containing 12 videos related to FIFA 2010 (South Africa) was used for experiments. Experimental results show that precision and recall of our method is 90.9% and 90.9%, respectively. They also illustrate that the proposed method outperforms other methods for goal event detection.