Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dissimilarity measures such as Dynamic Time Warping. For these general dissimilarity measures, k-nearest neighbor algorithms are a natural choice. Hover, k-NN classifiers are known to be sensitive to noise and outliers. , a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping DTW distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite PSD kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably,  obtained 89i¾?% accuracy on the ll-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect.
['Mohammad Ali Bagheri', 'Qigang Gao', 'Sergio Escalera']
I am writing a research paper on a idea Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels
[{"from": "human", "value": "I am writing a research paper on a idea Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dissimilarity measures such as Dynamic Time Warping. For these general dissimilarity measures, k-nearest neighbor algorithms are a natural choice. Hover, k-NN classifiers are known to be sensitive to noise and outliers. , a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping DTW distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite PSD kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably,  obtained 89i\u00be?% accuracy on the ll-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect."}]