View-Invariant Gesture Recognition Using Nonparametric Shape Descriptor

Chen

et al. 2015

IET Computer Vision

Self Cite

Traditional studies in vision‐based hand gesture recognition remain rooted in view‐dependent representations, and hence users are forced to be fronto‐parallel to the camera. To solve this problem, view‐invariant gesture recognition aims to make the recognition result independent of viewpoint changes. However, in current works the view‐invariance is achieved at the price of mixing different gesture patterns that have similar trajectory curve shape but different semantic meanings. For example, the gesture ‘push’ can be mistaken as ‘drag’ from another viewpoint. To address this shortcoming, in this study, the authors use a shape descriptor to extract the view‐invariant features of a three‐dimensional (3D) trajectory. As the shape features are invariant to omnidirectional viewpoint changes, the orientation features are then added into weight different rotation angles so that similar trajectory shapes are better separated. The proposed method was conducted on two different databases, including a popular Australian Sign Language database and a challenging Kinect Hand Trajectory database. Experimental results show that the proposed algorithm achieves a higher average recognition rate than the state‐of‐the‐art approaches, and can better distinguish confusing gestures while meeting the view‐invariant condition.

Section: Rotation-invariant Point Matching Results Between Templates ...mentioning

confidence: 99%

“…Shape descriptor is well suited to solve the viewpoint problem. The shape of a trajectory is determined by the spatial relationship between all its points, so the set of point-to-point distances (PPD) preserve all the shape information [32]. We call this kind of shape descriptors the 'complete shape descriptor'.…”

Section: Shape Descriptormentioning

confidence: 99%

Trajectory‐based view‐invariant hand gesture recognition by fusing shape and orientation

Chen

et al. 2015

IET Computer Vision

Self Cite

“…Some special cases will not downgrade the performance a lot in practice. In our previous work [27], the proposed method is equal to set  as n, which proves to be a redundant representation that brings heavy computation burden while contributing little to the precision.…”

Section: Lemma 1 Given a Set Of N Different Pointsmentioning

confidence: 99%

Point Context: An Effective Shape Descriptor for RST-Invariant Trajectory Recognition

Chen

et al. 2016

J Math Imaging Vis

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Abstract-Motion trajectory recognition is important for characterizing the moving property of an object. The speed and accuracy of trajectory recognition rely on a compact and discriminative feature representation, and the situations of varying rotation, scaling and translation has to be specially considered. In this paper we propose a novel feature extraction method for trajectories. Firstly a trajectory is represented by a proposed point context, which is a rotation-scale-translation (RST) invariant shape descriptor with a flexible tradeoff between computational complexity and discrimination, yet we prove that it is a complete shape descriptor. Secondly, the point context is nonlinearly mapped to a subspace by Kernel Nonparametric Discriminant Analysis (KNDA) to get a compact feature representation, and thus a trajectory is projected to a single point in a low-dimensional feature space. Experimental results show that, the proposed trajectory feature shows encouraging improvement than state-of-art methods.

“…When the effect of scale is removed by dividing the mean value, C k × n is a rotation‐scale‐translation‐invariant shape descriptor. The difference of this work from Wu et al [28] is that the descriptor is represented as a vector form, yet in this work we use a matrix C k × n to reorganise the distance features, which is more suitable for tensor algebra.…”

Section: Action Representation and Recognitionmentioning

confidence: 99%

Human action recognition based on tensor shape descriptor

et al. 2016

IET Computer Vision

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Human action recognition is an important task. This study presents an efficient framework for recognising action with a 3D skeleton kinematic joint model in less computational time for practical usage. First, a tensor shape descriptor (TSD) is proposed in this study, which takes advantage of the spatial independence of body joints, avoids a lot of difficult problem of the explicit motion estimation required in traditional methods, reserves the spatial information of each frame. Thus, the new TSD is a complete and view‐invariant descriptor. Second, a novel tensor dynamic time warping (TDTW) method is proposed to measure joint‐to‐joint similarity of 3D skeletal body joints locally in the temporal extent, which is implemented by extending DTW to that of two multiway data arrays (or tensors). Then, a multi‐linear projection process is employed to map the TSD to a low‐dimensional tensor subspace, which is classified by the nearest neighbour classifier. The experiment results on the public action data set (MSR‐Action3D) and motion capture data set (CMU_Mocap) show that the proposed method can achieve a comparable or better performance in recognition accuracy compared with the state‐of‐the‐art approaches.