Deep learning for behaviour recognition in surveillance applications

Maria Andersson

doi:10.1117/12.2533764

7 October 2019 Deep learning for behaviour recognition in surveillance applications

Maria Andersson

Proceedings Volume 11166, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III; 111660T (2019) https://doi.org/10.1117/12.2533764
Event: SPIE Security + Defence, 2019, Strasbourg, France

Abstract

Machine learning has recently made great progress in object classification, and especially object classification in images. In some cases, machine learning has even shown better ability than the human. There is a great potential to partially, or fully, automate sensor data analysis in surveillance systems. The automation could greatly facilitate the human operator’s work in finding critical information. After the success of object classification in images, the next step is to try to achieve similar progress in behaviour recognition using similar approaches. The paper includes a brief state-of-the-art for automatic behaviour recognition, and especially behaviours related to surveillance. The focus is on approaches based on deep learning. The paper presents an experiment with a specific deep learning algorithm, namely the long short-term memory (LSTM). For behaviours in surveillance applications, actual training data are rare. One approach for overcoming this is to use a deep learning algorithm that is pre-trained for a nearby application, and then transferred to the current application. Another is to expand the amount of training data using simulations. A difficulty with simulations is to create data that have similar characteristics as real sensor data, that is, include relevant noise and uncertainties. The paper briefly discusses the case of simulated data for the development of a deep learning method.

Conference Presentation

Citation Download Citation

Maria Andersson "Deep learning for behaviour recognition in surveillance applications", Proc. SPIE 11166, Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III, 111660T (7 October 2019); https://doi.org/10.1117/12.2533764

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