Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences is very useful for a variety of tasks, such as video surveillance, objectlevel video summarization, video indexing, digital library organization, etc. However, it remains a challenging task for computers to achieve robust action recognition due to cluttered background, camera motion, occlusion, and geometric and photometric variances of objects. For example, in a live video of a skating competition, the skater moves rapidly across the rink, and the camera also moves to follow the skater. With moving camera, non-stationary background, and moving target, few vision algorithms could identify, categorize and

We present a framework for automatically enhancing videos of a static scene using a few photographs of the same scene. For example, our system can transfer photographic qualities such as high resolution, high dynamic range and better lighting from the photographs to the video. Additionally, the user can quickly modify the video by editing only a few still images of the scene. Finally, our system allows a user to remove unwanted objects and camera shake from the video. These capabilities are enabled by two technical contributions presented in this paper. First, we make several improvements to a state-of-the-art multiview stereo algorithm in order to compute view-dependent depths using video, photographs, and structure-from-motion data. Second, we present a novel image-based rendering algorithm that can re-render the input video using the appearance of the photographs while preserving certain temporal dynamics such as specularities and dynamic scene lighting.

We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and intermediate topics corresponding to human action categories. This is achieved by using a probabilistic Latent Semantic Analysis (pLSA) model. Given a novel video sequence, the model can categorize and localize the human action(s) contained in the video. We test our algorithm on two challenging datasets: the KTH human action dataset and a recent dataset of figure skating actions. Our results are on par or slightly better than the best reported results. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions. 1