Deep learning-based intelligent surveillance model for detection of anomalous activities from videos
by Karishma Pawar; Vahida Attar
International Journal of Computational Vision and Robotics (IJCVR), Vol. 10, No. 4, 2020

Abstract: For safeguarding and monitoring purposes, public places are equipped with surveillance cameras. Timely and accurate identification of suspicious activities is paramount to securing the public places. Assigning human personnel to keep continuous watch over ongoing activities is error-prone and laborious. To alleviate the need of human personnel for monitoring such videos, automated surveillance systems are required. This paper proposes a deep learning based intelligent surveillance model for detection of anomalous activities. The problem of anomaly detection has been handled as one class classification problem. The proposed approach involves two dimensional convolutional auto-encoder for feature learning, sequence-to-sequence long short term memory model for learning temporal statistical correlation and radial basis function as activation function in fully connected network for one class classification. We experimented on real-world dataset by two variants of proposed approach and achieved significant results at frame-level anomaly detection.

Online publication date: Fri, 01-May-2020

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 Computational Vision and Robotics (IJCVR):
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