Video sequence modeling by dynamic Bayesian networks: a systematic approach from coarse-to-fine grains
Ying Luo, Jenq–Neng Hwang · 2004
A dynamic Bayesian network (DBN) based framework to model video sequences is proposed. The video sequences of interest include single-shot video sequences containing only one event and multishot video sequences containing various events. By taking advantage of the temporal continuity of video sequences and assuming Markovian property between successive image frames, we propose DBNs as the tool to map low-level features to high-level concepts. The feasibility of DBN modeling is tested on single-and multishot video sequences. Specifically, a coarse-grained video interpretation framework based on one kind of DBN, the hierarchical hidden Markov model (HHMM), is proposed for multishot video sequences. For single-shot video sequences, we present a fine-grained object based interpretation and classification system based on another version of DBNs. The preliminary simulations show great promise on the efficiency and flexibility of using DBNs for video sequence modeling.