Detection of weapon possession and fire in Public Safety surveillance cameras

The employment of video surveillance cameras by public safety agencies enables incident detection in monitored cities by using object detection for scene description, enhancing the protection to the general public. Object detection has its drawbacks, such as false positives. Our work aims to enhance object detection and image classification by employing IoU (Intersection over Union) to minimize the false positives and identify weapon holders or fire in a frame, adding more information to the scene.

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Accelerating real‐time object detection in high‐resolution video surveillance

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.6307 ◽

2021 ◽

Author(s):

Yuefeng Wang ◽

Kuang Mao ◽

Tong Chen ◽

Yanglong Yin ◽

Shuibing He ◽

...

Keyword(s):

High Resolution ◽

Object Detection ◽

Real Time ◽

Video Surveillance

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Data Augmentation Methods Applying Grayscale Images for Convolutional Neural Networks in Machine Vision

Applied Sciences ◽

10.3390/app11156721 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6721

Author(s):

Jinyeong Wang ◽

Sanghwan Lee

Keyword(s):

Neural Networks ◽

Machine Vision ◽

Object Detection ◽

Image Classification ◽

Convolutional Neural Networks ◽

Data Augmentation ◽

Image Data ◽

Manufacturing Productivity ◽

Smart Factories ◽

Grayscale Images

In increasing manufacturing productivity with automated surface inspection in smart factories, the demand for machine vision is rising. Recently, convolutional neural networks (CNNs) have demonstrated outstanding performance and solved many problems in the field of computer vision. With that, many machine vision systems adopt CNNs to surface defect inspection. In this study, we developed an effective data augmentation method for grayscale images in CNN-based machine vision with mono cameras. Our method can apply to grayscale industrial images, and we demonstrated outstanding performance in the image classification and the object detection tasks. The main contributions of this study are as follows: (1) We propose a data augmentation method that can be performed when training CNNs with industrial images taken with mono cameras. (2) We demonstrate that image classification or object detection performance is better when training with the industrial image data augmented by the proposed method. Through the proposed method, many machine-vision-related problems using mono cameras can be effectively solved by using CNNs.

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A Complex for Monitoring Transport Infrastructure Facilities Based on Video Surveillance Cameras and Laser Scanners

Transportation Research Procedia ◽

10.1016/j.trpro.2021.02.130 ◽

2021 ◽

Vol 54 ◽

pp. 775-782

Author(s):

Dmitry Gura ◽

Ivan Markovskii ◽

Nafset Khusht ◽

Irina Rak ◽

Saida Pshidatok

Keyword(s):

Video Surveillance ◽

Transport Infrastructure ◽

Laser Scanners ◽

Surveillance Cameras

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Dangerous Scenes Recognition During Hoisting Based on Faster Region-Based Convolutional Neural Network

Volume 2: Mechanics and Behavior of Active Materials; Structural Health Monitoring; Bioinspired Smart Materials and Systems; Energy Harvesting; Emerging Technologies ◽

10.1115/smasis2018-8226 ◽

2018 ◽

Author(s):

Hongguo Su ◽

Mingyuan Zhang ◽

Shengyuan Li ◽

Xuefeng Zhao

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Image Classification ◽

State Of The Art ◽

Spatial Location ◽

Average Precision ◽

Practical Applications ◽

Security Enhancement ◽

Human Interactions

In the last couple of years, advancements in the deep learning, especially in convolutional neural networks, proved to be a boon for the image classification and recognition tasks. One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects or scenes can be identified automatically, then a lot of accidents can be prevented. For this purpose, in this paper we made use of state-of-the-art implementation of Faster Region-based Convolutional Neural Network (Faster R-CNN) based on the monitoring video of hoisting sites to train a model to detect the dangerous object and the worker. By extracting the locations of them, object-human interactions during hoisting, mainly for changes in their spatial location relationship, can be understood whereby estimating whether the scene is safe or dangerous. Experimental results showed that the pre-trained model achieved good performance with a high mean average precision of 97.66% on object detection and the proposed method fulfilled the goal of dangerous scenes recognition perfectly.

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