Noninvasive Passenger Detection Comparison Using Thermal Imager and IP Cameras (original) (raw)
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Pedestrian detection for underground mine vehicles using thermal images
IEEE Africon '11, 2011
Mine vehicles are a leading cause of mining fatalities. A reliable anti-collision system is needed to prevent vehicle-personnel collisions. The proposed collision detection system uses the fusion of a three-dimensional (3D) sensor and thermal infrared camera for human detection and tracking. In addition to a thermal camera, a distance sensor will provide depth information and allow the calculation of the vehicle and pedestrian velocities. The results of subsystem tests show that a simple temperature range is sufficient for segmentation and a neural network shows the best classification results in terms of speed and accuracy. Results of initial tests performed on two different 3D sensors show a significant disadvantage to the use of time of flight cameras in a mine environment.
Mining Scince, 2019
Within the INESI-project (Increasing Efficiency and Safety Improvement in Underground Mining Transportation Routes) long-wavelength infrared (LWIR) cameras are used for detecting persons on underground belt conveyors or within hazardous areas e.g. in front of crusher or skip vessels by the project partners KOMAG and the Institute for Advanced Mining Technologies (AMT). The test case for evaluating the performance of thermal imaging regarding these applications is the Polish Sobieski underground coal mine operated by Tauron mining company. By the development of thermal image processing algorithms, an automated detection of persons and classification of different objects was achieved. This may allow implementing smart services for person detection on underground belt conveyors as well as material characterization between coal, rock and disturbing objects on belt conveyors.
Real-time Crowd Monitoring using Infrared Thermal Video Sequences
Monitoring people in a crowded environment is a critical task in civilian surveillance. Most vision-based counting techniques depend on detecting individuals in order to count their number. Counting becomes inefficient when it is required in real-time and when the crowd is dense. This paper proposes a novel technique for monitoring and estimating the density of crowd in real-time using infrared thermal video sequences. The research targets monitoring the crowd in Muslims’ pilgrimage event (Hajj) while almost 3.0 million Muslims gather in Makkah to perform Hajj. During different Hajj phases the movement of the gathered Muslims is required at the same time from a place to another. Thus monitoring their crowd in real-time is crucial in order to take immediate decisions to prevent crowd disasters. A state of the art thermal camera has been acquired for the surveillance process. In addition, special software modules have been developed to analyze the captured thermographic video sequences in real-time. The results show high accuracy of the estimation of the crowd density in real-time.
Human detection for underground autonomous mine vehicles using thermal imaging
2011
Underground mine automation has the potential to increase safety, productivity and allow the mining of lower-grade resources. In a mining environment with both autonomous robots and humans, it is essential that the robots are able to detect and avoid people. Current pedestrian detection systems and the reasons that they are inadequate for mining robots are discussed. A system for human detection in underground mines, using a fusion of threedimensional (3D) information with thermal imaging, is proposed. The system extracts regions of interest and classifies them as human or background. The scene excluding the pedestrians is assumed to be static and is intended to be used to determine the ego motion of the vehicle. In addition to the thermal camera, a distance sensor will provide depth information and allow the calculation of the vehicle and pedestrian velocities. Various classification methods are compared and it is shown that a neural network provides the best results in terms of speed and accuracy. The results of tests on two 3D sensors indicate that further work is required to determine the effect of the harsh environment on the accuracy of the sensors.
Automated Decision Technique for the Crowd Estimation Method Using Thermal Videos
Applied Computational Intelligence and Soft Computing, 2022
Counting and detecting the pedestrians is an important and critical aspect for several applications such as estimation of crowd density, organization of events, individual's fow control, and surveillance systems to prevent the difculties and overcrowding in a huge gathering of pedestrians such as the Hajj occasion, which is the annual event for Muslims with the growing number of pilgrims every year. Tis paper is based on applying some enhancements to two diferent techniques for automatically estimating the crowd density. Tese two approaches are based on individual motion and the body's thermal features. Teessential characteristic of crowd counting techniques is that they do not require a previously stored and trained data; instead they use a live video stream as input. Also, it does not require any intervention from individuals. So, this feature makes it easy to automatically estimate the crowd density. What makes this work special than other approaches in literature is the use of thermal videos, and not just relying on a way or combining several ways to get the crowd size but also analyzing the results to decide which approach is better considering diferent cases of scenes. Tis work aims at estimating the crowd density using two methods and decide which method is better and more accurate depending on the case of the scene; i.e., this work measures the crowd size from videos using the heat signature and motion analysis of the human body, plus using the results analysis of both approaches to decide which approach is better. Te better approach can vary from video-to-video according to many factors such as the motion state of humans in this video, the occlusion amount, etc. Both approaches are discussed in this paper. Te frst one is based on capturing the thermal features of an individual and the second one is based on detecting the features of an individual motion. Te result of these approaches has been discussed, and diferent experiments were conducted to prove and identify the most accurate approach. Te experimental results prove the advancement of the approach proposed in this paper over the literature as indicated in the result section.
Detecting Rails and Obstacles Using a Train-Mounted Thermal Camera
Lecture Notes in Computer Science, 2015
We propose a method for detecting obstacles on the railway in front of a moving train using a monocular thermal camera. The problem is motivated by the large number of collisions between trains and various obstacles, resulting in reduced safety and high costs. The proposed method includes a novel way of detecting the rails in the imagery, as well as a way to detect anomalies on the railway. While the problem at a first glance looks similar to road and lane detection, which in the past has been a popular research topic, a closer look reveals that the problem at hand is previously unaddressed. As a consequence, relevant datasets are missing as well, and thus our contribution is twofold: We propose an approach to the novel problem of obstacle detection on railways and we describe the acquisition of a novel data set.
Automatic detection of vehicle occupants: the imaging problemand its solution
Machine Vision and Applications, 2000
The automatic detection and counting of vehicle occupants is a challenging research problem that was given little attention until recently. An automated vehicle-occupant-counting system would greatly facilitate the operation of freeway lanes reserved for car pools (high occupancy vehicle lanes or HOV lanes). There are three major aspects of this problem: (a) the imaging aspect (sensor phenomenology), (b) the pattern recognition aspect, and (c) the system architecture aspect. In this paper, we present a solution to the imaging aspect of the problem. We propose a novel system based on fusion of near-infrared imaging signals and we demonstrate its adequacy with theoretical and experimental arguments. We also compare our solution to other possible solutions across the electromagnetic spectrum, particularly in the thermal infrared and visible regions.
An Improved Technique for Crowd Counting Based on Thermal Bands
Menoufia Journal of Electronic Engineering Research
The estimation and the controlling processes in the crowd counting filed are the most important on over applications related to surveillance systems and control follow related peoples to give them a complete safety especially in the large groups that contain a huge collection of individuals. There were found various events in human histories that gather a huge number of crowds in the same place. For example, in religion occasions as in HAJJ season which occurs every year, these events cause accidents that lead to death. So, to avoid and prevent all types of accidents related to these huge crowds; this paper presents an improved technique to estimate the density of peoples in the same place to help the decision makers to monitor and control the pedestrians overcrowded in the large collection of individuals. The proposed technique depends on the thermal bands for human, according to the big variance of temperature human body between the skin and the skin covered with clothes. Also, it presents the whole range of the temperature for each frame in the video. The essential characteristic of crowd counting technique is that it does not require a previously stored and trained data, but it uses a live video stream as input. Also, it does not require any intervention from individuals. The research's approach depends on capturing the thermal features of an individual. The result of this technique is introduced and proved to be highly accurate, and the experimental results demonstrate the effectiveness of the approach.
An Effective Surveillance System Using Thermal Camera
Thermography, or thermal visualization is a type of infrared visualization. Thermographic cameras are used in many heavy factories like metal recycling factories, wafer production factories and etc for monitoring the temperature conditions of the machines. Besides, thermographic camera can be used to detect trespassers in environment with poor lighting condition, whereby, the conventional digital cameras are less applicable in. In this paper, we proposed two simple and fast detection algorithms into a cost effective thermal imaging surveillance system. This surveillance system not only used in monitoring the functioning of different machinery and electrical equipments in a factory site, it can also used for detecting the trespassers in poor lighting condition. Experimental results show that the proposed surveillance system achieves high accuracy in monitoring machines conditions and detecting trespassers.