TV commercial detection using constrained viterbi algorithm based on time distribution

TV Commercials play an important role in our lives, and automatic commercial detection is very useful in TV video analysis. Most of previous works focus on visual and audio features of commercial, while ignoring the information of distributions of commercial blocks in different program types and broadcast times. In this paper, we propose a novel method to fuse visual, audio features and global characteristics to detect commercial blocks. Firstly, visual and audio features such as FMPI (Image Frames Marked with Product Information) are utilized to predict the probabilities of commercial shot using SVM classifier. And then, these output probabilities are regarded as observations of a Markov Chain of commercial shots. At last, a viterbi algorithm with time constraints, which are modeled the distributions of duration and inter-arrival time of commercial blocks with GMM (Gaussian Mixture Model), is applied to search the optimal path of commercial shots. Experiments get promising performance on a real TV video database, and show that distributions of duration and inter-arrival time of commercial blocks are good characteristics to capture global feature of commercial blocks.