Suspicious activity detection using deep learning in secure assisted living IoT environments

2020 ◽  
Author(s):  
G. Vallathan ◽  
A. John ◽  
Chandrasegar Thirumalai ◽  
SenthilKumar Mohan ◽  
Gautam Srivastava ◽  
...  
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Activity Detection ◽  
Suspicious Activity

scholarly journals SUSPICIOUS ACTIVITY DETECTION USING CCTV SURVEILLANCE VIDEO

2021 ◽  
Vol 6 (22) ◽  
pp. 60-70
Author(s):  
Bushra Yasmeen ◽  
Haslina Arshad ◽  
Hameedur Rahman
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Training Model ◽  
Activity Detection ◽  
Surveillance Video ◽  
Full Support ◽  
Cctv Surveillance ◽  
Developed World ◽  
Suspicious Activity

Security has recently been given the highest priority with the rise in the number of antisocial activations taking place. To continuously track individuals and their interactions, CCTVs have been built in several ways. Every person is recorded on an image on average 30 times a day in a developed world with a community of 1.6 billion. The resolution of 710*570 captured at knitting will approximate 20 GB per day. Constant monitoring of human data makes it hard to judge whether the incident is an irregular one, and it is an almost uphill struggle when a population and its full support are needed. In this paper, we make a system for the detection of suspicious activity using CCTV surveillance video. There seems to be a need to demonstrate in which frame the behavior is located as well as which section of it allows the faster judgment of the suspicious activity is unusual. This is done by converting the video into frames and analyzing the persons and their activates from the processed frames. We have accepted wide support from Machine learning and Deep Learning Algorithms to make it possible. To automate that process, first, we need to build a training model using a large number of images (all possible images which describe features of suspicious activities) and a “Convolution Neural Network‟ using the Tensor Flow Python module. We can then upload any video into the application, and it will extract frames from the uploaded video and then that frame will be applied on a training model to predict its class such as suspicious or normal.

Deep Learning Techniques for Koala Activity Detection

2018 ◽  
Author(s):  
Ivan Himawan ◽  
Michael Towsey ◽  
Bradley Law ◽  
Paul Roe
Keyword(s):  
Deep Learning ◽  
Activity Detection ◽  
Learning Techniques

Human Activity Detection Using Deep Learning and Bracelet with Bluetooth Transmitter

2021 ◽  
Author(s):  
Stevan Cakic ◽  
Stevan Sandi ◽  
Daliborka Nedic ◽  
Srdan Krco ◽  
Tomo Popovic
Keyword(s):  
Deep Learning ◽  
Human Activity ◽  
Activity Detection ◽  
Human Activity Detection

Deep learning approaches for video-based anomalous activity detection

2018 ◽  
Vol 22 (2) ◽  
pp. 571-601 ◽  
Author(s):  
Karishma Pawar ◽  
Vahida Attar
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
Activity Detection

Forecasting Trends in an Ambient Assisted Living Environment Using Deep Learning

2021 ◽  
Author(s):  
Guillaume Gingras ◽  
Mehdi Adda ◽  
Abdenour Bouzouane ◽  
Hussein Ibrahim ◽  
Clemence Dallaire
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Ambient Assisted Living ◽  
Living Environment

scholarly journals Comparing Video Activity Classifiers within a Novel Framework

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1545
Author(s):  
Chiman Kwan ◽  
Bence Budavari ◽  
Bulent Ayhan
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
General Framework ◽  
Experimental Results ◽  
Analysis Tool ◽  
Activity Detection ◽  
Rule Based ◽  
Event Classification ◽  
Hybrid Combination ◽  
Rule Based Approach

Video activity classification has many applications. It is challenging because of the diverse characteristics of different events. In this paper, we examined different approaches to event classification within a general framework for video activity detection and classification. In our experiments, we focused on event classification in which we explored a deep learning-based approach, a rule-based approach, and a hybrid combination of the previous two approaches. Experimental results using the well-known Video Image Retrieval and Analysis Tool (VIRAT) database showed that the proposed classification approaches within the framework are promising and more research is needed in this area

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