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Manifold Learning for ToF-based Human Body Tracking and Activity Recognition

Recent technological advances have lead to the development of cameras that measure depth by means of the time-of-flight (ToF) principle [5]. ToF cameras allow capturing an entire scene instantaneously, and thus provide depth images in real-time. Despite the relatively low resolution, this type of data offers a clear advantage over conventional cameras for specific applications, such as human-machine interaction. In this paper, we propose a method that allows simultaneously recognizing the performed activity and tracking the full-body pose of a person observed by a a single ToF camera. Our method removes the need for identifying body parts in sparse and noisy ToF images [4] or for fitting a skeleton using expensive optimisation techniques [1]. The proposed method consists of learning a prior model of human motion and using an efficient, sampling-based inference approach for activity recognition and body tracking (Figure 1). The prior motion model is comprised of a set of low-dimensional manifold embeddings for each activity of interest. We generate the embeddings from full-body pose training data using a manifold learning technique [2]. Each of the embeddings acts as a low-dimensional parametrisation of feasible body poses [3] that we use to constrain the problem of body tracking only from depth cues. In a generative tracking framework, we sample the low-dimensional manifold embedding space by means of a particle filter and thus avoid exhaustively searching the full-body pose space. This way, we are able to track multiple pose hypotheses for different activities and to select one that is most consistent with the observed depth cues. Our depth feature descriptor, intuitively a sparse 3D human silhouette representation, can easily be extracted from ToF images. The overall method combines the distinctiveness of multiple local, activity-specific motion models into a global model capable of recognising and tracking multiple activities from simple observations.