Skeleton-based Human Activity Recognition for Video Surveillance
khalifa, mohamed essam; Ahmed Taha; Hala Zayed; El-Sayed M. El-Horbarty;
Abstract
Recognizing human activity is one of the important areas of computer vision research today. It plays a vital role in constructing
intelligent surveillance systems. Despite the efforts in the past decades, recognizing human activities from videos is still a challenging task.
Human activity may have different forms ranging from simple actions to complex activities. Recently released depth cameras provide
effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this paper, a system for human activity
recognition is proposed. We have considered the task of obtaining a descriptive labeling of the activities being performed through labeling
human sub-activities. The activities we consider happen over a long period, and comprise several sub-activities performed in a sequence.
The proposed activity descriptor makes the activity recognition problem viewed as a sequence classification problem. The proposed
system employs Hidden Markov Models (HMMs) to recognize human activities. The system is evaluated on two benchmark datasets for
daily living activity recognition. Experiment results demonstrate that the proposed system outperforms the state-of-the-art methods.
intelligent surveillance systems. Despite the efforts in the past decades, recognizing human activities from videos is still a challenging task.
Human activity may have different forms ranging from simple actions to complex activities. Recently released depth cameras provide
effective estimation of 3D positions of skeletal joints in temporal sequences of depth maps. In this paper, a system for human activity
recognition is proposed. We have considered the task of obtaining a descriptive labeling of the activities being performed through labeling
human sub-activities. The activities we consider happen over a long period, and comprise several sub-activities performed in a sequence.
The proposed activity descriptor makes the activity recognition problem viewed as a sequence classification problem. The proposed
system employs Hidden Markov Models (HMMs) to recognize human activities. The system is evaluated on two benchmark datasets for
daily living activity recognition. Experiment results demonstrate that the proposed system outperforms the state-of-the-art methods.
Other data
Title | Skeleton-based Human Activity Recognition for Video Surveillance | Authors | khalifa, mohamed essam ; Ahmed Taha; Hala Zayed; El-Sayed M. El-Horbarty | Keywords | Activity Recognition;Behavior Analysis;Depth Images;HMM;MSVM;RGB-D;Video Surveillance | Issue Date | Jan-2015 | Publisher | IJSER | Journal | International Journal of Scientific & Engineering Research | Volume | 6 | Start page | 993 | End page | 1004 | DOI | 10.14299/ijser.2015.01.024 |
Attached Files
File | Description | Size | Format | Existing users please Login |
---|---|---|---|---|
Skeleton-based Human Activity.pdf | 1.41 MB | Adobe PDF | Request a copy |
Similar Items from Core Recommender Database
Items in Ain Shams Scholar are protected by copyright, with all rights reserved, unless otherwise indicated.