Mr. Abbas Mahbod Profile

Abbas Mahbod

at Univ of Calgary

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 April 2020 Paper

A deep learning based methodology for video anomaly detection in crowded scenes

Abbas Mahbod, Henry Leung

Proceedings Volume 11413, 114130I (2020) https://doi.org/10.1117/12.2558889

KEYWORDS: Video, Computer programming, Optical flow, Data modeling, Video surveillance, Neurons, Video acceleration, Reconstruction algorithms, Flame detectors, Detection and tracking algorithms

Read Abstract +

Different approaches have been proposed in the literature for anomaly detection in image/video area. Traditional methods such as trajectory or spatio-temporal based techniques rely on hand-crafted features. The occlusion problems and high complexity in crowded scenes are the vital drawbacks behind using these methodologies. Deep learning structures proved recently to be useful for defining effective solutions for anomaly detection where the high level features are learnt and selected automatically. However, the block-wise methods such as CNNs are computationally slow. On the other hand, they are totally supervised learning methodologies, while the video-based anomaly detection is an unsupervised problem. Auto- Encoders (Convolutional AE, vibrational AE, etc.) can be considered as an alternative option. This paper presents a stateof- the-art deep learning algorithm to be applied in such an unsupervised problem. Using the basic concepts behind the Auto-Encoders as a well-known unsupervised learning algorithm, we propose a novel methodology to detect and localize the anomalies in a video scene. The presented network is trained based on the normal patterns during training phase. The proposed structure enables the system to capture the 2D structure in image sequences during the learning process. The working hypothesis is that a deep network is able to learn normal events in videos, and, therefore, the difference of normal and anomalous frames can be used for devising an anomaly score. The simulation results on well-known data sets such as UCSD confirm that the proposed methodology achieves high performance results in case of accuracy and total processing time compared with counterpart approaches.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years