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In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.

The current multi-camera systems have not studied the problem of person tracking under low resolution constraints. In this paper, we propose a low resolution sensor network for person tracking. The network is composed of cameras with a resolution of 30×30 pixels. The multi-camera system is used to evaluate probability occupancy mapping and maximum likelihood trackers against ground truth collected by ultra-wideband (UWB) testbed. Performance evaluation is performed on two video sequences of 30 minutes. The experimental results show that maximum likelihood estimation based tracker outperforms the state-of-the-art on low resolution cameras.

Nowadays, an important problem in multi-camera systems is how to select the camera with the best frontal view of a person in order to visualize to an observer. Therefore, we present a minimum score based criterion for best view selection, based on face recognition with geometric features. In this approach, faces are represented with Curve Edge Maps (CEMs), which are collections of polynomial curves with a convex region. Face recognition is performed by matching face CEMs driven by histograms of intensities and histograms of relative positions. The resulting face recognition scores are employed as quality-of-view measures. They indicate whether or not persons are seen by cameras in frontal view. Experiments show that the method is robust and efficient when selecting the best view in a multi-camera system. Furthermore, our method outperforms view selection based on face detection only.

There is a rapidly growing demand for cameras containing built-in intelligence for various purposes such as surveillance and identification. Face recognition is an important application for these cameras. Previous research has shown that faces can be well represented by parabola segments using an algorithm which fits parabola segments to edge pixels. Faces are then recognized using a technique which matches parabola segments based on distance and intensity. Considerable computational resources are required for the extraction of the parabola primitives, due to the need for Canny edge detection. This algorithm is well-suited for the new generation of massively parallel SmartCams though, if it is represented in a highly parallelized, low complexity representation adapted to the characteristics of these SmartCam architectures. This paper proposes such an implementation of the Canny edge detection algorithm for the Single Instruction Multiple Data (SIMD) Xetal IC3D processor, resulting in a real-time performance using a smart camera not bigger than a typical surveillance camera.