Scalable local features and hybrid classifiers for improving action recognition

Abstract

 In recent years, action recognition techniques have played an increasingly important role in autonomous systems. However, the computational costs and precision of action recognition algorithms are still major challenges. Recently, a deep learning approach was proposed to obtain a higher accuracy, but large and deep neural networks have high computational costs. This paper presents a new approach that allows for a significant reduction in computational time while slightly increasing the accuracy. The contribution consists of two parts: a scalable feature extraction method (SFE) and a hybrid model of different classifiers. First, the SFE method is proposed for application to histogram orientation-based feature descriptors, such as the histogram of orientated gradient (HOG), histogram of optical flow (HOF), and the motion boundary histogram (MBH). An advantage of SFE is its ability to quickly compute features. Scalable feature extraction enables accurate approximation of features extracted from traditional image pyramids by efficiently using only the original image. Our method is inspired by a special data structure used for storing basic information of optical flow and image gradients, which are computed from the original image and then used to extract features across multiple scales of the feature region without recomputing the image gradients and optical flow. Second, we focus on a hybrid classification method based on a linear support vector machine (SVM) and hidden conditional random field (HCRF) model that improves the recognition precision. This effort shows that a combination of SVM and HCRF models provides a better accuracy than the traditional approaches. Experimental results illustrate that the proposed approach allows for both a significant reduction in computational time and an improved accuracy.