Daily Activity Recognition with Skeletal Descriptor subject to Spatio-Temporal Execution Variability
Date Issued
2015
Date
2015
Author(s)
Yang, Hsing-Lin
Abstract
Human activity recognition has become one of the most important areas of research in computer vision. Without extra markers attached to human body, the interaction between machine and human can be natural in a vision-based recognition system. However, it still remains some challenges such as intra-class variability and inter-class similarity. In order to solve the problems, this thesis presents a novel skeleton-based activity recognition methodology with depth sensors. Due to the variation of execution styles and speed for different individuals, a Gaussian blur mask is applied to model the joint position variation while dynamic time warping (DTW) is employed to align the temporal sequences. Besides encoding the structure, the motion is also characterized. Through recording the joint angle trajectory, the entropy of each joint can be evaluated and then be combined with the projected velocity feature. Moreover, the support vector machine (SVM) is applied to acquire the classification results. In our implementation, two challenging public datasets are used to simulate real situations in our daily living. The experimental results show that the proposed approach is discriminative for human activity recognition and performs better than state-of-the-arts. This approach can benefit to several applications such as human-machine interaction (HMI).
Subjects
Activity recognition
Activity of daily living
intra-class variability
inter-class similarity
Type
thesis
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