Unlike traditional third-person cameras mounted on robots, a first-person camera, captures a person's visual sensorimotor object interactions from up close. In this paper, we study the tight interplay between our momentary visual attention and motor action with objects from a first-person camera. We propose a concept of action-objects---the objects that capture person's conscious visual (watching a TV) or tactile (taking a cup) interactions. Action-objects may be task-dependent but since many tasks share common person-object spatial configurations, action-objects exhibit a characteristic 3D spatial distance and orientation with respect to the person. We design a predictive model that detects action-objects using EgoNet, a joint two-stream network that holistically integrates visual appearance (RGB) and 3D spatial layout (depth and height) cues to predict per-pixel likelihood of action-objects. Our network also incorporates a first-person coordinate embedding, which is designed to learn a spatial distribution of the action-objects in the first-person data. We demonstrate EgoNet's predictive power, by showing that it consistently outperforms previous baseline approaches. Furthermore, EgoNet also exhibits a strong generalization ability, i.e., it predicts semantically meaningful objects in novel first-person datasets. Our method's ability to effectively detect action-objects could be used to improve robots' understanding of human-object interactions.
 We present an approach which exploits the coupling between human actions and scene geometry to use human pose as a cue for single-view 3D scene understanding. Our method builds upon recent advances in still-image pose estimation to extract functional and geometric constraints on the scene. These constraints are then used to improve single-view 3D scene understanding approaches. The proposed method is validated on monocular time-lapse sequences from YouTube and still images of indoor scenes gathered from the Internet. We demonstrate that observing people performing different actions can significantly improve estimates of 3D scene geometry. Unsupervised categorization of objects is a fundamental problem in computer vision. While appearance-based methods have become popular recently, other important cues like functionality are largely neglected. Motivated by psychological studies giving evidence that human demonstration has a facilitative effect on categorization in infancy, we propose an approach for object categorization from depth video streams. To this end, we have developed a method for capturing human motion in real-time. The captured data is then used to temporally segment the depth streams into actions. The set of segmented actions are then categorized in an un-supervised manner, through a novel descriptor for motion capture data that is robust to subject variations. Furthermore, we automatically localize the object that is manipulated within a video segment, and categorize it using the corresponding action. For evaluation, we have recorded a dataset that comprises depth data with registered video sequences for 6 subjects, 13 action classes, and 174 object manipulations. In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to incorporate semantic object context as a post-processing step into any off-the-shelf object categorization model. Using a conditional random field (CRF) framework, our approach maximizes object label agreement according to contextual relevance. We compare two sources of context: one learned from training data and another queried from Google Sets. The overall performance of the proposed framework is evaluated on the PASCAL and MSRC datasets. Our findings conclude that incorporating context into object categorization greatly improves categorization accuracy. This paper investigates object categorization according to function, i.e., learning the affordances of objects from human demonstration. Object affordances (functionality) are inferred from observations of humans using the objects in different types of actions. The intended application is learning from demonstration, in which a robot learns to employ objects in household tasks, from observing a human performing the same tasks with the objects. We present a method for categorizing manipulated objects and human manipulation actions in context of each other. The method is able to simultaneously segment and classify human hand actions, and detect and classify the objects involved in the action. This can serve as an initial step in a learning from demonstration method. Experiments show that the contextual information improves the classification of both objects and actions. Detecting objects in cluttered scenes and estimating articulated human body parts are two challenging problems in computer vision. The difficulty is particularly pronounced in activities involving human-object interactions (e.g. playing tennis), where the relevant object tends to be small or only partially visible, and the human body parts are often self-occluded. We observe, however, that objects and human poses can serve as mutual context to each other – recognizing one facilitates the recognition of the other. In this paper we propose a new random field model to encode the mutual context of objects and human poses in human-object interaction activities. We then cast the model learning task as a structure learning problem, of which the structural connectivity between the object, the overall human pose, and different body parts are estimated through a structure search approach, and the parameters of the model are estimated by a new max-margin algorithm. On a sports data set of six classes of human-object interactions [12], we show that our mutual context model significantly outperforms state-of-the-art in detecting very difficult objects and human poses. We propose an approach to activity recognition based on detecting and analyzing the sequence of objects that are being manipulated by the user. In domains such as cooking, where many activities involve similar actions, object-use information can be a valuable cue. In order for this approach to scale to many activities and objects, however, it is necessary to minimize the amount of human-labeled data that is required for modeling. We describe a method for automatically acquiring object models from video without any explicit human supervision. Our approach leverages sparse and noisy readings from RFID tagged objects, along with common-sense knowledge about which objects are likely to be used during a given activity, to bootstrap the learning process. We present a dynamic Bayesian network model which combines RFID and video data to jointly infer the most likely activity and object labels. We demonstrate that our approach can achieve activity recognition rates of more than 80 on a real-world dataset consisting of 16 household activities involving 33 objects with significant background clutter. We show that the combination of visual object recognition with RFID data is significantly more effective than the RFID sensor alone. Our work demonstrates that it is possible to automatically learn object models from video of household activities and employ these models for activity recognition, without requiring any explicit human labeling. Psychologists have proposed that many human-object interaction activities form unique classes of scenes. Recognizing these scenes is important for many social functions. To enable a computer to do this is however a challenging task. Take people-playing-musical-instrument (PPMI) as an example; to distinguish a person playing violin from a person just holding a violin requires subtle distinction of characteristic image features and feature arrangements that differentiate these two scenes. Most of the existing image representation methods are either too coarse (e.g. BoW) or too sparse (e.g. constellation models) for performing this task. In this paper, we propose a new image feature representation called “grouplet”. The grouplet captures the structured information of an image by encoding a number of discriminative visual features and their spatial configurations. Using a dataset of 7 different PPMI activities, we show that grouplets are more effective in classifying and detecting human-object interactions than other state-of-the-art methods. In particular, our method can make a robust distinction between humans playing the instruments and humans co-occurring with the instruments without playing. Existing methods for video scene analysis are primarily concerned with learning motion patterns or models for anomaly detection. We present a novel form of video scene analysis where scene element categories such as roads, parking areas, sidewalks and entrances, can be segmented and categorized based on the behaviors of moving objects in and around them. We view the problem from the perspective of categorical object recognition, and present an approach for unsupervised learning of functional scene element categories. Our approach identifies functional regions with similar behaviors in the same scene and or across scenes, by clustering histograms based on a trajectory-level, behavioral codebook. Experiments are conducted on two outdoor webcam video scenes with low frame rates and poor quality. Unsupervised classification results are presented for each scene independently, and also jointly where models learned on one scene are applied to the other. We present a human-centric paradigm for scene understanding. Our approach goes beyond estimating 3D scene geometry and predicts the "workspace" of a human which is represented by a data-driven vocabulary of human interactions. Our method builds upon the recent work in indoor scene understanding and the availability of motion capture data to create a joint space of human poses and scene geometry by modeling the physical interactions between the two. This joint space can then be used to predict potential human poses and joint locations from a single image. In a way, this work revisits the principle of Gibsonian affor-dances, reinterpreting it for the modern, data-driven era. The visual perception of object affordances has emerged as a useful ingredient for building powerful computer vision and robotic applications [31]. In this paper we introduce a novel approach to reason about liquid containability - the affordance of containing liquid. Our approach analyzes container objects based on two simple physical processes: the Fill and Transfer of liquid. First, it reasons about whether a given 3D object is a liquid container and its best filling direction. Second, it proposes directions to transfer its contained liquid to the outside while avoiding spillage. We compare our simplified model with a common fluid dynamics simulation and demonstrate that our algorithm makes human-like choices about the best directions to fill containers and transfer liquid from them. We apply our approach to reason about the containability of several real-world objects acquired using a consumer-grade depth camera. We investigate a discriminatively trained model of person-object interactions for recognizing common human actions in still images. We build on the locally order-less spatial pyramid bag-of-features model, which was shown to perform extremely well on a range of object, scene and human action recognition tasks. We introduce three principal contributions. First, we replace the standard quantized local HOG SIFT features with stronger discriminatively trained body part and object detectors. Second, we introduce new person-object interaction features based on spatial co-occurrences of individual body parts and objects. Third, we address the combinatorial problem of a large number of possible interaction pairs and propose a discriminative selection procedure using a linear support vector machine (SVM) with a sparsity inducing regularizer. Learning of action-specific body part and object interactions bypasses the difficult problem of estimating the complete human body pose configuration. Benefits of the proposed model are shown on human action recognition in consumer photographs, outperforming the strong bag-of-features baseline. Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those objects. While each of these can be conducted independently, recognition improves when interactions between these elements are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which unifies the inference processes involved in object classification and localization, action understanding and perception of object reaction. Traditional approaches for object classification and action understanding have relied on shape features and movement analysis respectively. By placing object classification and localization in a video interpretation framework, we can localize and classify objects which are either hard to localize due to clutter or hard to recognize due to lack of discriminative features. Similarly, by applying context on human movements from the objects on which these movements impinge and the effects of these movements, we can segment and recognize actions which are either too subtle to perceive or too hard to recognize using motion features alone.