Real-time abnormal object detection for video surveillance in smart cities
With the adaptation of video surveillance in many areas for object detection, monitoring
abnormal behavior in several cameras requires constant human tracking for a single camera …
abnormal behavior in several cameras requires constant human tracking for a single camera …
Weapon detection in real-time cctv videos using deep learning
Security and safety is a big concern for today's modern world. For a country to be
economically strong, it must ensure a safe and secure environment for investors and tourists …
economically strong, it must ensure a safe and secure environment for investors and tourists …
Improving video surveillance systems in banks using deep learning techniques
In the contemporary world, security and safety are significant concerns for any country that
wants to succeed in tourism, attracting investors, and economics. Manually, guards …
wants to succeed in tourism, attracting investors, and economics. Manually, guards …
Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance
The capability of distinguishing between small objects when manipulated with hand is
essential in many fields, especially in video surveillance. To date, the recognition of such …
essential in many fields, especially in video surveillance. To date, the recognition of such …
Real-time object detection using pre-trained deep learning models MobileNet-SSD
A Younis, L Shixin, S Jn, Z Hai - … of 2020 6th International Conference on …, 2020 - dl.acm.org
Mobile networks and binary neural networks are the most commonly used techniques for
modern deep learning models to perform a variety of tasks on embedded systems. In this …
modern deep learning models to perform a variety of tasks on embedded systems. In this …
Object detection based on multi-layer convolution feature fusion and online hard example mining
J Chu, Z Guo, L Leng - IEEE access, 2018 - ieeexplore.ieee.org
Object detection is a significant issue in visual surveillance. Faster region-based
convolutional neural network (R-CNN) is a typical object detection algorithm of deep …
convolutional neural network (R-CNN) is a typical object detection algorithm of deep …
Multi-object detection in traffic scenes based on improved SSD
X Wang, X Hua, F Xiao, Y Li, X Hu, P Sun - Electronics, 2018 - mdpi.com
In order to solve the problem that, in complex and wide traffic scenes, the accuracy and
speed of multi-object detection can hardly be balanced by the existing object detection …
speed of multi-object detection can hardly be balanced by the existing object detection …
A semi-supervised deep learning based video anomaly detection framework using RGB-D for surveillance of real-world critical environments
Video surveillance has become very important in the current era of smart cities. Large
amounts of surveillance cameras are deployed at public and private places for surveillance …
amounts of surveillance cameras are deployed at public and private places for surveillance …
Object detection for crime scene evidence analysis using deep learning
Object detection is the key module in most visual-based surveillance applications and
security systems. In crime scene analysis, the images and videos play a significant role in …
security systems. In crime scene analysis, the images and videos play a significant role in …
A comprehensive review on deep learning-based methods for video anomaly detection
Video surveillance systems are popular and used in public places such as market places,
shopping malls, hospitals, banks, streets, education institutions, city administrative offices …
shopping malls, hospitals, banks, streets, education institutions, city administrative offices …