AN ALGORITHM FOR SMOKE ROF DETECTION BASED ON SURVEILLANCE VIDEO

2013 ◽  
Vol 22 (03) ◽  
pp. 1350010 ◽  
Author(s):  
XU ZHANG ◽  
JIANBIN XIE ◽  
WEI YAN ◽  
QIANYI ZHONG ◽  
TONG LIU
Keyword(s):  
Detection Algorithm ◽  
Experimental Results ◽  
Surveillance Video ◽  
Dynamic Features ◽  
Smoke Detection ◽  
Perception Model ◽  
Limited Application

In this paper, an algorithm for smoke region of focus (ROF) detection based on surveillance video is proposed in order to solve the problem of limited application in scenes range and imaging environment of the traditional smoke detection algorithm. The frog vision perception model is used in this algorithm. First the suspect regions are detected, and then the static and the dynamic features of the regions are chosen for the smoke ROF detection. Experimental results show that the algorithm is efficient and significant for improving the operational rate of the detection.

An Algorithm of early Smoke Detection Based on Multiple Features

2012 ◽  
Vol 457-458 ◽  
pp. 1254-1257
Author(s):  
Ming Xin Jiang ◽  
Xing Yang Cai ◽  
Hong Yu Wang
Keyword(s):  
Diffusion Process ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Algorithm ◽  
Time Requirement ◽  
Dynamic Features ◽  
Smoke Detection ◽  
Multiple Features ◽  
Color Distribution ◽  
Good Detection

An early smoke detection algorithm based on Codebook model and multiple features is presented in this paper. First, the foreground is obtained by using the Codebook algorithm. Second, the model of color distribution and the model of shape feathers of smoke are applied to detect the suspected smoke area in the foreground. Finally, the false alarm rate is reduced effectively by using dynamic features in the diffusion process of smoke. Experimental results show that our algorithm has good detection performance and achieves real-time requirement which is very important for real application.

scholarly journals Distributed Beacon Drifting Detection for Localization in Unstable Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming Xia ◽  
Peiliang Sun ◽  
Xiaoyan Wang ◽  
Yan Jin ◽  
Qingzhang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Information ◽  
Detection Accuracy ◽  
Research Issue ◽  
Fundamental Research

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

scholarly journals A Video Based Fire Smoke Detection Using Robust AdaBoost

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3780 ◽  
Author(s):  
Xuehui Wu ◽  
Xiaobo Lu ◽  
Henry Leung
Keyword(s):  
Smoke Detector ◽  
Dynamic Features ◽  
Smoke Detection ◽  
Motion Direction ◽  
Direction Change ◽  
Satisfactory Performance ◽  
Orientation Histogram ◽  
Fire Smoke ◽  
Motion Speed ◽  
Camera Sensors

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

scholarly journals AN OPTIMIZED SURF-BASED FIRE AND SMOKE DETECTION SYSTEM USING IMAGE PROCESSING FROM SURVEILLANCE VIDEO.

2020 ◽  
Vol 9 (10) ◽  
pp. 86-96
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optimization Approach ◽  
Surveillance Video ◽  
Smoke Detection ◽  
Video Frames ◽  
Optimal Feature

Over the last decades, digital image processing based fire and smoke detection have been improving steadily to provide a more accurate detection results in the area of surveillance security system. Detection of the fire and smoke from the surveillance videos is very challenging task due to the complex structural properties of the video frames or images and need improvisation in the existing work by utilization of feature selection or optimization approach to select on optimal feature according to the fire and smoke. A research based on the combination of various feature extraction techniques with feature selection approach for fire and smoke detection has been presented in this paper. In this research, we develop Fire and Smoke Detection (FSD) system using digital image processing with the concept of Speed up Robust Feature (SURF) along with the Intelligent Water Drops (IWD) as a feature selection and optimization algorithm. Here, Artificial Neural Network (ANN) is used as an Artificial Intelligence (AI) technique with that helps to select a set of optimal feature from the extracted by SURF descriptor from the video frames. By utilizing the concept of optimized ANN, the accuracy of proposed FSD system is increases in terms of detection accuracy and with minimum percentage of error. At last, the performance of the FSD system is calculated to validate the model and this shows that it is possible to use IWD with SURF as a feature extraction technique in order to detect the fire or smoke form the surveillance video with minimum error rate and the simulation results clearly show the effectiveness of proposed FSD system

scholarly journals Early smoke detection of forest fires based on SVM image segmentation

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 150-159
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Smoke Detection ◽  
Large Space ◽  
Merging Algorithm ◽  
Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

scholarly journals A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zuopeng Zhao ◽  
Zhongxin Zhang ◽  
Xinzheng Xu ◽  
Yi Xu ◽  
Hualin Yan ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Identification Accuracy ◽  
Convolution Kernel ◽  
Small Target ◽  
Resource Constrained ◽  
Embedded Devices

It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.

Medical Space Oriented Color Psychology Perception Model

2014 ◽  
Vol 587-589 ◽  
pp. 461-467 ◽  
Author(s):  
Chun Fu Li ◽  
Hui Ting Shi
Keyword(s):  
Health System ◽  
Age Groups ◽  
Psychological Needs ◽  
Experimental Results ◽  
Perfect Health ◽  
Color Design ◽  
Therapeutic Benefits ◽  
Perception Model ◽  
Color Psychology ◽  
Auxiliary Role

Reasonable color design in medical space can play an auxiliary role in patients’ treatment by researching and analyzing the differences of color psychological needs during patients with different age groups being treated. So this paper proposes a medical space oriented color psychology perception model to present how color design in medical space affects patients’ therapeutic benefits. This model can figuratively reflect the color needs of patients with different age groups and affects patients’ therapeutic benefits by triggering patients’ psychology perception. At last, the experimental results validate the effectiveness of medical space oriented color psychology perception model and present the color need tendency in medical space of patients with different age groups, which plays an important role to improve and perfect health system.

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