Human behaviour recognition algorithm based on improved DMM and Fisher coding
by Wei Feng; Ji-liang Zhang; Li Peng
International Journal of Modelling, Identification and Control (IJMIC), Vol. 32, No. 3/4, 2019

Abstract: Human behaviour recognition has become a key technology in intelligent sensing, location and tracking tasks. In view of the different speed of action execution and DMM loss of time dimension information, this paper proposes a human action recognition method based on improved DMM and Fisher coding. First, in consideration of the different action speeds of long and short video, this paper adopts two different video segmentation strategies. Second, in order to make video-based DMM contain more time dimension information, this paper proposes an improved DMM; then, in order to better express the texture information of the image, this paper improves the extraction of LBP features by DMM. Finally, due to the different feature lengths and high dimensions obtained by the long and short video, this paper adopts the Fisher vector for feature encoding and combines SVM to complete the action recognition. In the public action recognition database MSRAction3D and gesture recognition database MSRGesture3D, the accuracy rate of the algorithm is 96.25% and 96.00%, respectively, and it has higher recognition rate than many existing algorithms.

Online publication date: Mon, 18-Nov-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com