OVERFIT PREVENTION IN HUMAN MOTION DATA BY ARTIFICIAL NEURAL NETWORK

Motion analysis has been an active research area for the past decade. Several approaches had been proposed to detect and recognize motion activity for different applications such as motion estimation, modeling, and reconstruction. However, a suitable classifier is required to be embedded with the surveillance system to ensure accurate motion recognition. During these processes, the recognition system compares the captured motion with the motion database in order to recognize the motion activity. However, the classifier can only recognize the motion activities that are closely fit with the database, and overfitting has been an issue in this process. Hence, this paper is aimed at resolving overfitting problem by using Artificial Neural Network (ANN) for motion classification. The motion data was transformed into numerical data with an aid of Kinovea. Data mining software called WEKA was used to perform motion classification. Multi-Layer Perceptron (MLP), which is known as ANN, was modified to recognize different motion activities in the classification process. It was observed that MLP is able to yield classification accuracy of 97.62%. Overfitting issues were also solved by manipulating learning rates in the ANN classifier. A reduced learning rate from 0.3 to 0.1 improved the classification accuracy of jumping motion by up to 12.04%

Human action recognition by feature-reduced Gaussian process classification

This paper presents a spectral analysis-based feature-reduced Gaussian Processes (GP) classification approach to recognition of articulated and deformable human actions from image sequences. Using Tensor Subspace Analysis (TSA), space-time human silhouettes extracted from action sequences are transformed to a low dimensional multivariate time series, from which structure-based statistical features are extracted to summarize the action properties. GP classification, based on spectrally reduced features, is then applied to learn and predict action categories. Experimental results on two real-world state-of-the-art datasets show that the GP classification outperforms a Support Vector Machine (SVM). In particular, spectral feature reduction can effectively eliminate the inconsistent features, while leaving performance undiminished. Moreover, compared with Automatic Relevance Determination (ARD), the spectral way for feature reduction is more efficient. © 2009 Elsevier B.V. All rights reserved.Hang Zhou, Liang Wang and David Sute