A Modified Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing

Robust, fast and reliable examination of the surroundings is essential for further advancements in autonomous driving and robotics. Time-of-Flight (ToF) camera sensors are a key technology to measure surrounding objects and their distances on a pixel basis in real-time. Environmental effects, like rain in front of the sensor, can influence the distance accuracy of the sensor. Here we use an optical ray-tracing based procedure to examine the rain effect on the ToF image. Simulation results are presented for experimental rain droplet distributions, characteristic of intense rainfall at rates of 25 mm/h and 100 mm/h. The ray-tracing based simulation data and results serve as an input for developing and testing rain signal suppression strategies.

Download Full-text

Fast and robust detection and tracking of multiple persons on RGB-D data fusing spatio-temporal information

2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) ◽

10.1109/mfi.2015.7295752 ◽

2015 ◽

Author(s):

Sebastian Starke ◽

Norman Hendrich ◽

Hannes Bistry ◽

Jianwei Zhang

Keyword(s):

Temporal Information ◽

Robust Detection ◽

Detection And Tracking ◽

Spatio Temporal ◽

Data Fusing

Download Full-text

A Video Based Fire Smoke Detection Using Robust AdaBoost

Sensors ◽

10.3390/s18113780 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3780 ◽

Cited By ~ 11

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.

Download Full-text

Shallow Water Bathymetry Inversion Using Hybrid Multi-band Composites Resulted from Integration of Drone-based RGB and Multispectral Imagery

10.20944/preprints202112.0414.v2 ◽

2022 ◽

Author(s):

Evangelos Alevizos ◽

Athanasios V Argyriou ◽

Dimitris Oikonomou ◽

Dimitrios D Alexakis

Keyword(s):

Low Cost ◽

Visible Spectrum ◽

Small Scale ◽

Multispectral Imagery ◽

Depth Data ◽

Recent Developments ◽

Model Tuning ◽

Inversion Techniques ◽

Camera Sensors ◽

Current Inversion

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum is available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery, as an alternative to costly hyperspectral sensors. Combining drone-based RGB and multispectral imagery into a single cube dataset, provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types. The use of suitable end-member spectra which are representative of the seafloor types of the study area and the sun zenith angle are important parameters in model tuning. The results of this study show good correlation (R2>0.7) and less than half a meter error when they are compared with sonar depth data. Consequently, integration of various drone-based imagery may be applied for producing centimetre resolution bathymetry maps at low cost for small-scale shallow areas.

Download Full-text

On the Development of Autonomous Vehicle Safety Distance by an RSS Model Based on a Variable Focus Function Camera

Sensors ◽

10.3390/s21206733 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6733

Author(s):

Min-Joong Kim ◽

Sung-Hun Yu ◽

Tong-Hyun Kim ◽

Joo-Uk Kim ◽

Young-Min Kim

Keyword(s):

Autonomous Vehicles ◽

Focal Length ◽

Autonomous Vehicle ◽

Autonomous Driving ◽

Cruise Control ◽

Radar Sensors ◽

Safety And Reliability ◽

Time Required ◽

Variable Focus ◽

Camera Sensors

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

Download Full-text

A Fusion Approach for Pre-Crash Scenarios based on Lidar and Camera Sensors

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) ◽

10.1109/vtc2021-spring51267.2021.9449039 ◽

2021 ◽

Author(s):

Daniel Vriesman ◽

Marcelo Eduardo Pederiva ◽

Jose Mario De Martino ◽

Alceu Britto Junior ◽

Alessandro Zimmer ◽

...

Keyword(s):

Camera Sensors ◽

Fusion Approach

Download Full-text

Cooling Agents' Effect Monitoring When Applied to Skin of Healthy Human Subjects

Innovative Research in Thermal Imaging for Biology and Medicine - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-2072-6.ch004 ◽

2017 ◽

pp. 79-92

Author(s):

Francis J Ring ◽

Carl Jones ◽

Kurt Ammer ◽

Peter Plassmann ◽

Ricardo Vardasca ◽

...

Keyword(s):

Thermal Imaging ◽

Sports Injuries ◽

Human Subjects ◽

Regions Of Interest ◽

Healthy Human ◽

Monitoring Method ◽

Camera Sensors ◽

Examination Room ◽

Objective Tool ◽

Over Time

There are cooling products available for relieving the pain of minor sports injuries, in muscles, tendons, joints, strains, sprains and knocks. These products are based on ice, gel and cold patches. In order to quantify objectively the effect of each type of those products thermal imaging was used. This monitoring method is suitable to quantify quickly large regions of interest in skin areas over time through the thermal radiation perceived by thermal camera sensors. All recorded images were taken in a controlled environment and following a standard capture protocol in terms of subject, equipment and examination room preparation and procedure of conduction the examination. Two experiments were performed. The obtained results demonstrate that quantitative thermal imaging is a simple and objective tool for evaluating topical cooling treatments. However, it is important to assess the emissivity of any applied substance, which could have a significant effect on temperature measurement by remote sensing.

Download Full-text

Study of Noise Removal Techniques for Digital Images

Biometrics ◽

10.4018/978-1-5225-0983-7.ch044 ◽

2017 ◽

pp. 1105-1144

Author(s):

Punyaban Patel ◽

Bibekananda Jena ◽

Bibhudatta Sahoo ◽

Pritam Patel ◽

Banshidhar Majhi

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

Statistical Models ◽

Noise Removal ◽

Image Degradation ◽

Linear Filters ◽

Restoration Techniques ◽

Filtering Technique ◽

High Level ◽

Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Download Full-text

Study of Noise Removal Techniques for Digital Images

Emerging Technologies in Intelligent Applications for Image and Video Processing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9685-3.ch003 ◽

2016 ◽

pp. 48-87

Author(s):

Punyaban Patel ◽

Bibekananda Jena ◽

Bibhudatta Sahoo ◽

Pritam Patel ◽

Banshidhar Majhi

Keyword(s):

Image Processing ◽

Gaussian Noise ◽

Statistical Models ◽

Noise Removal ◽

Image Degradation ◽

Linear Filters ◽

Restoration Techniques ◽

Filtering Technique ◽

High Level ◽

Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Download Full-text