Algorithm and Architecture Design Using HON4D for Online Human Action Recognition
Date Issued
2015
Date
2015
Author(s)
Hsu, Chia-Jung
Abstract
The ultimate goal of computer vision is to help computing devices understand the real world, process visual information efficiently, and even have semantic understandings like humans do. Nowadays, computer vision algorithms progressed rapidly, and developed plenty innovative applications. For example, intelligent environmental surveillances of the future are capable of monitoring real environments, including objects and people. Through the release of Kinect, 3D sequences become more accessible, and push researches forward to the ultimate goal. In the past few years, various methods have been proposed to solve the problem of human activity recognition from depth images. Compared with traditional 2D videos, depth sequences provide geometrical information, and therefore can better describe the scenes. In this thesis, we aim to provide an online action recognition system using 3D data. Since depth sequences are captured with a single commodity camera, noise and occlusion are common problems. In order to deal with these issues, we extract histogram of oriented 4D surface normal (HON4D) features, which can capture the joint shape-motion cues in the depth sequence. Moreover, we present an automatic segmentation method for online recognition of depth sequences. The overall framework is mainly separated into two parts, feature extraction engine, and histogram engine. According to our run-time profiling, feature extraction is the most time-consuming part. Therefore, HON4D feature extraction is implemented with several approximation techniques while maintaining its performance. Furthermore, we discuss three online action recognition architecture using HON4D features. These online action recognition architectures are based on direct sliding window, modified cell-based sliding window, and our proposed algorithm. In sum, we implement HON4D feature extraction to optimize the most time-consuming part in our proposed system. Furthermore, an online action recognition framework is proposed. Compared with other sliding window methods, our framework is favored for lower memory consumption, and also bandwidth.
Subjects
Online action recognition framework
Histogram of oriented 4D normals
Machine learning
SDGs
Type
thesis