A Space-Time Graph Optimization Approach Based on Maximum Cliques for Action Detection

We present an efficient action detection method that takes a space-time (ST) graph optimization approach for real-world videos. Given an ST graph representing the entire action video, our method identifies a maximum-weight connected subgraph (MWCS) indicating an action region by applying an optimization approach based on clique information. We define an energy function based on maximum weight cliques for subregions of the graph and formulate it using an optimization problem that can be represented as a linear system. Our energy function includes the maximum and connectivity properties for finding the MWCS, and its optimization solution indicates the probability of belonging to the maximum subgraph for each node. Our graph optimization method efficiently solves the detection problem by applying the clique-based approach and simple linear system solver. We demonstrate that our detection method results in a more accurate localization compared with conventional methods through our experimental results with real-world data sets, such as the Hollywood and MSR action data sets. We also show that our method outperforms the state-of-the-art methods of action detection.