Deep learning architecture for recognition of abnormal activities

Marwa Khatrouch; Mariem Gnouma; Ridha Ejbali; Mourad Zaied

doi:10.1117/12.2314834

13 April 2018 Deep learning architecture for recognition of abnormal activities

Marwa Khatrouch, Mariem Gnouma, Ridha Ejbali, Mourad Zaied

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960F (2018) https://doi.org/10.1117/12.2314834
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

Abstract

The video surveillance is one of the key areas in computer vision researches. The scientific challenge in this field involves the implementation of automatic systems to obtain detailed information about individuals and groups behaviors. In particular, the detection of abnormal movements of groups or individuals requires a fine analysis of frames in the video stream. In this article, we propose a new method to detect anomalies in crowded scenes. We try to categorize the video in a supervised mode accompanied by unsupervised learning using the principle of the autoencoder. In order to construct an informative concept for the recognition of these behaviors, we use a technique of representation based on the superposition of human silhouettes. The evaluation of the UMN dataset demonstrates the effectiveness of the proposed approach.

Citation Download Citation

Marwa Khatrouch, Mariem Gnouma, Ridha Ejbali, and Mourad Zaied "Deep learning architecture for recognition of abnormal activities", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960F (13 April 2018); https://doi.org/10.1117/12.2314834

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available