Learning Deep C3D Features For Soccer Video Event Detection

Soccer video event identification has been an interesting task in research community during past few decades. Numerous machine learning techniques and C2D (Convolution 2-dimensional) have been used for this problem, but C3D has not been implemented for this task. By taking advantage from C3D (Convolution 3-dimensional), to completely exploit spatio-temporal relation, deep convolution network is developed to highlight distinct video events in proposed research work. Initially, we detect soccer video event marks by pixel differencing and edge change ratio. After this semantic features of segmented frames are extracted followed by CNN to map soccer event categories: Corner, Shoot, Goal Attempt, Penalty Kick. Because no effective and suitable dataset is available, we categorized soccer videos into four classes and developed soccer videos dataset for training CNN network. Evaluation results on soccer match clips generated results with high efficiency.