Algorithm and Architecture Design for Real-time Human Action Recognition
Date Issued
2014
Date
2014
Author(s)
Chi, Chung-Yu
Abstract
Computer vision has been developed for decades, and has totally changed our lives. Thanks to the progress of technologies, we have entered the era of big data and smart devices. With the help of machine learning algorithms, electronic devices are able to learn knowledge from big data such as the Internet. The combination of computer vision and machine learning has also brought a large amount of applications, making our lives more convenient. The ultimate goal of computer vision is to invent a brilliant robot which perceives and interacts just like human-beings. Understanding the semantic meaning behind videos is the first step toward this goal.
Rather than still images, videos including spatial-temporal information imply richer knowledge. Therefore, human action recognition becomes a basic application that can be implemented in the vision of robots. The fact that different variations in videos increases the difficulty of analysis, leading many researchers to develop better algorithms aiming at raising the recognition accuracy on datasets. However, the computation complexity of feature extraction in videos is still too complicated to be real-time in past researches.
In the thesis, we first introduce some applications and fundamental functions of computer vision. These algorithms, such as corner detection and feature extraction, are very important since they construct the basis of recognition task. Adapting the 2D successful object recognition framework into 3D videos, we face additional challenges. Comparing several related algorithms and examining the pros and cons of each method, we choose to use space-time local features in our approach.
Considering both the efficiency and accuracy, MoFREAK feature is extracted to generate robust descriptors of action videos. MoFREAK is a feature combining the appearance model and motion model independently. We characterize static information by FREAK and dynamic information by MIP, and show good performance through datasets. Analyzing the computation time of entire procedure, it is feasible for real-time applications. Then the experiment results of proposed action recognition system is described thoroughly to prove the robustness and efficiency. Finally, to adapt to high resolution and online situations, an innovative sliding histogram scheme is developed.
To sum up, a real-time online action recognition system is designed. We can recognize different actions instantly due to the proposed fast feature extraction and matching algorithm.
Subjects
影片處理
視覺動態特徵選取
即時動作辨識
線上辨識架構
滑動式累計直方圖
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R01943023-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):0946c2681bdea6b148ea7c6985cf62a7