In real settings, natural body movements can be erroneously recognized by whole-body input systems as explicit input actions. We call body activity not intended as input actions "background activity." We argue that understanding background activity is crucial to the success of always-available whole-body input in the real world. To operationalize this argument, we contribute a reusable study methodology and software tools to generate standardized background activity datasets composed of data from multiple Kinect cameras, a Vicon tracker, and two high-definition video cameras. Using our methodology, we create an example background activity dataset for a television-oriented living room setting. We use this dataset to demonstrate how it can be used to redesign a gestural interaction vocabulary to minimize conflicts with the real world. The software tools and initial living room dataset are publicly available (this http URL).
Recent technological advances in connected-speech recognition and position sensing in space have encouraged the notion that voice and gesture inputs at the graphics interface can converge to provide a concerted, natural user modality. The work described herein involves the user commanding simple shapes about a large-screen graphics display surface. Because voice can be augmented with simultaneous pointing, the free usage of pronouns becomes possible, with a corresponding gain in naturalness and economy of expression. Conversely, gesture aided by voice gains precision in its power to reference. Over the years, a large number of methods have been proposed to analyze human pose and motion information from images, videos, and recently from depth data. Most methods, however, have been evaluated on datasets that were too specific to each application, limited to a particular modality, and more importantly, captured under unknown conditions. To address these issues, we introduce the Berkeley Multimodal Human Action Database (MHAD) consisting of temporally synchronized and geometrically calibrated data from an optical motion capture system, multi-baseline stereo cameras from multiple views, depth sensors, accelerometers and microphones. This controlled multimodal dataset provides researchers an inclusive testbed to develop and benchmark new algorithms across multiple modalities under known capture conditions in various research domains. To demonstrate possible use of MHAD for action recognition, we compare results using the popular Bag-of-Words algorithm adapted to each modality independently with the results of various combinations of modalities using the Multiple Kernel Learning. Our comparative results show that multimodal analysis of human motion yields better action recognition rates than unimodal analysis. Vision-based human action recognition is the process of labeling image sequences with action labels. Robust solutions to this problem have applications in domains such as visual surveillance, video retrieval and human-computer interaction. The task is challenging due to variations in motion performance, recording settings and inter-personal differences. In this survey, we explicitly address these challenges. We provide a detailed overview of current advances in the field. Image representations and the subsequent classification process are discussed separately to focus on the novelties of recent research. Moreover, we discuss limitations of the state of the art and outline promising directions of research.  We present a first study of using RGB-D (Kinect-style) cameras for fine-grained recognition of kitchen activities. Our prototype system combines depth (shape) and color (appearance) to solve a number of perception problems crucial for smart space applications: locating hands, identifying objects and their functionalities, recognizing actions and tracking object state changes through actions. Our proof-of-concept results demonstrate great potentials of RGB-D perception: without need for instrumentation, our system can robustly track and accurately recognize detailed steps through cooking activities, for instance how many spoons of sugar are in a cake mix, or how long it has been mixing. A robust RGB-D based solution to fine-grained activity recognition in real-world conditions will bring the intelligence of pervasive and interactive systems to the next level.