The visual recognition of transitive actions comprising human-object interactions is a key component enabling artificial systems to operate in natural environments. This challenging task requires, in addition to the recognition of articulated body actions, the extraction of semantic elements from the scene such as the identity of the manipulated objects. In this paper, we present a self-organizing neural network for the recognition of human-object interactions from RGB-D videos. Our model consists of a hierarchy of Grow When Required (GWR) networks which learn prototypical representations of body motion patterns and objects, also accounting for the development of action-object mappings in an unsupervised fashion. To demonstrate this ability, we report experimental results on a dataset of daily activities collected for the purpose of this study as well as on a publicly available benchmark dataset. In line with neurophysiological studies, our self-organizing architecture shows higher neural activation for congruent action-object pairs learned during training sessions with respect to artificially created incongruent ones. We show that our model achieves good classification accuracy on the benchmark dataset in an unsupervised fashion, showing competitive performance with respect to strictly supervised state-of-the-art approaches.
One important goal of human activity recognition in machine learning and computer vision is to automatically detect and analyze human activities from the information acquired from visual sensing devices such as RGB cameras and range sensors. The literature suggests a conceptual categorization of human activities into four different levels depending on the complexity: gestures, actions, interactions, and group activities @cite_38 @cite_14 @cite_24 . Gestures are elementary movements of a person's body part and are the atomic components describing the meaningful motion of a person, e.g. or . Actions are single-person activities that may be composed of multiple gestures such as and . Interactions are human activities that involve a person and one (or more) objects. For instance, a is a human-object interaction. Finally, group activities are the activities performed by groups composed of multiple persons or objects, e.g. .
@cite_24 @cite_38 @cite_14
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Abstract Human activity recognition has been an important area of computer vision research since the 1980s. Various approaches have been proposed with a great portion of them addressing this issue via conventional cameras. The past decade has witnessed a rapid development of 3D data acquisition techniques. This paper summarizes the major techniques in human activity recognition from 3D data with a focus on techniques that use depth data. Broad categories of algorithms are identified based upon the use of different features. The pros and cons of the algorithms in each category are analyzed and the possible direction of future research is indicated. Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas. Abstract This paper presents an overview of state-of-the-art methods in activity recognition using semantic features. Unlike low-level features, semantic features describe inherent characteristics of activities. Therefore, semantics make the recognition task more reliable especially when the same actions look visually different due to the variety of action executions. We define a semantic space including the most popular semantic features of an action namely the human body (pose and poselet), attributes, related objects, and scene context. We present methods exploiting these semantic features to recognize activities from still images and video data as well as four groups of activities: atomic actions, people interactions, human–object interactions, and group activities. Furthermore, we provide potential applications of semantic approaches along with directions for future research.