Learning to Recognize Unsuccessful Activities Using a Two-Layer Latent Structural Model

In this paper, we propose to recognize unsuccessful activities (e.g., one tries to dress himself but fails), which have much more complex temporal structures, as we don't know when the activity performer fails (which is called the point of failure in this paper). We develop a two-layer latent structural SVM model to tackle this problem: the first layer specifies the point of failure, and the second layer specifies the temporal positions of a number of key stages accordingly. The stages before the point of failure are successful stages, while the stages after the point of failure are background stages. Given weakly labeled training data, our training algorithm alternates between inferring the two-layer latent structure and updating the structural SVM parameters. In recognition, our method can not only recognize unsuccessful activities, but also infer the latent structure. We demonstrate the effectiveness of our proposed method on several newly collected datasets.