Selection and context for action recognition

Recognizing human action in non-instrumented video is a challenging task not only because of the variability produced by general scene factors like illumination, background, occlusion or intra-class variability, but also because of subtle behavioral patterns among interacting people or between people and objects in images. To improve recognition, a system may need to use not only low-level spatio-temporal video correlations but also relational descriptors between people and objects in the scene. In this paper we present contextual scene descriptors and Bayesian multiple kernel learning methods for recognizing human action in complex non-instrumented video. Our contribution is threefold: (1) we introduce bag-of-detector scene descriptors that encode presence/absence and structural relations between object parts; (2) we derive a novel Bayesian classification method based on Gaussian processes with multiple kernel covariance functions (MKGPC), in order to automatically select and weight multiple features, both low-level and high-level, out of a large collection, in a principled way, and (3) perform large scale evaluation using a variety of features on the KTH and a recently introduced, challenging, Hollywood movie dataset. On the KTH dataset, we obtain 94.1% accuracy, the best result reported to date. On the Hollywood dataset we obtain promising results in several action classes using fewer descriptors and about 9.1% improvement in a previous benchmark test.1