Robust Behavior Recognition in Intelligent Surveillance Environments.

Sensors (Basel)

Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea.

Published: June 2016

Intelligent surveillance systems have been studied by many researchers. These systems should be operated in both daytime and nighttime, but objects are invisible in images captured by visible light camera during the night. Therefore, near infrared (NIR) cameras, thermal cameras (based on medium-wavelength infrared (MWIR), and long-wavelength infrared (LWIR) light) have been considered for usage during the nighttime as an alternative. Due to the usage during both daytime and nighttime, and the limitation of requiring an additional NIR illuminator (which should illuminate a wide area over a great distance) for NIR cameras during the nighttime, a dual system of visible light and thermal cameras is used in our research, and we propose a new behavior recognition in intelligent surveillance environments. Twelve datasets were compiled by collecting data in various environments, and they were used to obtain experimental results. The recognition accuracy of our method was found to be 97.6%, thereby confirming the ability of our method to outperform previous methods.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970060PMC
http://dx.doi.org/10.3390/s16071010DOI Listing

Publication Analysis

Top Keywords

intelligent surveillance
12
behavior recognition
8
recognition intelligent
8
surveillance environments
8
daytime nighttime
8
visible light
8
nir cameras
8
thermal cameras
8
robust behavior
4
environments intelligent
4

Similar Publications

Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.

View Article and Find Full Text PDF

To evaluate the predictive utility of N6-methyladenosine (m6A)-associated long non-coding RNAs (lncRNAs) for the prognosis and immunotherapy response in papillary renal cell carcinoma (pRCC). Transcriptomic data of pRCC samples were extracted from the TCGA database. The m6A-related lncRNAs were identified by Pearson correlation analysis.

View Article and Find Full Text PDF

Although conservative treatment is commonly used for osteoporotic vertebral fracture (OVF), some patients experience functional disability following OVF. This study aimed to develop prediction models for new-onset functional impairment following admission for OVF using machine learning approaches and compare their performance. Our study consisted of patients aged 65 years or older admitted for OVF using a large hospital-based database between April 2014 and December 2021.

View Article and Find Full Text PDF

Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.

BMC Public Health

December 2024

Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.

Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.

View Article and Find Full Text PDF

Association of sleep quality and its change with the risk of depression in middle-aged and elderly people: A 10-year cohort study from England.

J Affect Disord

December 2024

Department of Radiology, the Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.; Jiangxi Provincial Key Laboratory of Intelligent Medical Imaging, Nanchang, China.. Electronic address:

Background: Persistently poor sleep quality in young adults is linked to a higher risk of depression. However, the impact of changes in sleep quality on depression risk in middle-aged and older adults remain unclear. This study investigates the association between sleep quality, its changes, and the risk of depression in middle-aged and elderly people.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!