Stairway Detection Based on Single Camera by Motion Stereo

In this paper we are proposing a method for detecting the localization of indoor stairways. This is a fundamental step for the implementation of autonomous stair climbing navigation and passive alarm systems intended for the blind and visually impaired. Both of these kinds of systems must be able to recognize parameters that can describe stairways in unknown environments. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. We extracted the horizontal edge of the stairs by using the Gabor Filter. From the specified set of horizontal line segments, we extracted a hypothetical set of targets by using the correlation method. Finally, we used the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. As a result, testing was able to prove its effectiveness.In this paper we are proposing a method for detecting the localization of indoor stairways. This is a fundamental step for the implementation of autonomous stair climbing navigation and passive alarm systems intended for the blind and visually impaired. Both of these kinds of systems must be able to recognize parameters that can describe stairways in unknown environments. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. We extracted the horizontal edge of the stairs by using the Gabor Filter. From the specified set of horizontal line segments, we extracted a hypothetical set of targets by using the correlation method. Finally, we used the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. As a result, testing was able to prove its effectiveness

Simple and efficient method for calibration of a camera and 2D laser rangefinder

In the last few years, the integration of cameras and laser rangefinders has been applied to a lot of researches on robotics, namely autonomous navigation vehicles, and intelligent transportation systems. The system based on multiple devices usually requires the relative pose of devices for processing. Therefore, the requirement of calibration of a camera and a laser device is very important task. This paper presents a calibration method for determining the relative position and direction of a camera with respect to a laser rangefinder. The calibration method makes use of depth discontinuities of the calibration pattern, which emphasizes the beams of laser to automatically estimate the occurred position of laser scans on the calibration pattern. Laser range scans are also used for estimating corresponding 3D image points in the camera coordinates. Finally, the relative parameters between camera and laser device are discovered by using corresponding 3D points of them.In the last few years, the integration of cameras and laser rangefinders has been applied to a lot of researches on robotics, namely autonomous navigation vehicles, and intelligent transportation systems. The system based on multiple devices usually requires the relative pose of devices for processing. Therefore, the requirement of calibration of a camera and a laser device is very important task. This paper presents a calibration method for determining the relative position and direction of a camera with respect to a laser rangefinder. The calibration method makes use of depth discontinuities of the calibration pattern, which emphasizes the beams of laser to automatically estimate the occurred position of laser scans on the calibration pattern. Laser range scans are also used for estimating corresponding 3D image points in the camera coordinates. Finally, the relative parameters between camera and laser device are discovered by using corresponding 3D points of them

Combining Edge and One-Point RANSAC Algorithm to Estimate Visual Odometry

In recent years, classical structure from motion based SLAM has achieved significant results. Omnidirectional camera-based motion estimation has become interested researchers due to the lager field of view. This paper proposes a method to estimate the 2D motion of a vehicle and mapping by using EKF based on edge matching and one point RANSAC. Edge matching based azimuth rotation estimation is used as pseudo prior information for EKF predicting state vector. In order to reduce requirement parameters for motion estimation and reconstruction, the vehicle moves under nonholonomic constraints car-like structured motion model assumption. The experiments were carried out using an electric vehicle with an omnidirectional camera mounted on the roof. In order to evaluate the motion estimation, the vehicle positions were compared with GPS information and superimposed onto aerial images collected by Google map API. The experimental results showed that the method based on EKF without using prior rotation information given error is about 1.9 times larger than our proposed method.In recent years, classical structure from motion based SLAM has achieved significant results. Omnidirectional camera-based motion estimation has become interested researchers due to the lager field of view. This paper proposes a method to estimate the 2D motion of a vehicle and mapping by using EKF based on edge matching and one point RANSAC. Edge matching based azimuth rotation estimation is used as pseudo prior information for EKF predicting state vector. In order to reduce requirement parameters for motion estimation and reconstruction, the vehicle moves under nonholonomic constraints car-like structured motion model assumption. The experiments were carried out using an electric vehicle with an omnidirectional camera mounted on the roof. In order to evaluate the motion estimation, the vehicle positions were compared with GPS information and superimposed onto aerial images collected by Google map API. The experimental results showed that the method based on EKF without using prior rotation information given error is about 1.9 times larger than our proposed method

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanceOver the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performanc

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Stairway segmentation using Gabor Filter and vanishing point

This paper we are proposing to detect the localization and recognition of an indoor stairway. This is a fundamental step in the implementing of autonomous stair climbing navigation, as well as the implementing of passive alarm systems intended for the blind and visually impaired. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. The horizontal edges of the stairs are extracted by using the Gabor Filter. Then, the vanishing point is extracted from the specified set of line segments in the aim of facilitating the reconstruction of the stair treads. After this stage, we extract a hypothetical set of targets by using the correlation method. Finally, we employ the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. As a result, testing is able to prove its effectiveness.This paper we are proposing to detect the localization and recognition of an indoor stairway. This is a fundamental step in the implementing of autonomous stair climbing navigation, as well as the implementing of passive alarm systems intended for the blind and visually impaired. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. The horizontal edges of the stairs are extracted by using the Gabor Filter. Then, the vanishing point is extracted from the specified set of line segments in the aim of facilitating the reconstruction of the stair treads. After this stage, we extract a hypothetical set of targets by using the correlation method. Finally, we employ the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. As a result, testing is able to prove its effectiveness

Stairway tracking based on automatic target selection using directional filters

In this paper we are proposing to detect the localization and recognition of an indoor stairway. This is a fundamental step in the implementing of autonomous stair climbing navigation, as well as the implementing of passive alarm systems intended for the blind and visually impaired. Both of these systems must be able to recognize parameters that can describe stairways in unknown environments. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. We extracted the horizontal edge of the stairs by using the Gabor Filter. From the specified set of horizontal line segments, we extracted a hypothetical set of targets by using the correlation method. Finally, we are proposing the use of the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. After the stairway candidate region was obtained by applying our approach mentioned in the previous step, we proceeded with the candidate assessment tracking, based on the criterion of the minimum displaced frame difference, ground truth, as well as the rigidity of the stair. As a result, testing was able to prove its effectiveness.In this paper we are proposing to detect the localization and recognition of an indoor stairway. This is a fundamental step in the implementing of autonomous stair climbing navigation, as well as the implementing of passive alarm systems intended for the blind and visually impaired. Both of these systems must be able to recognize parameters that can describe stairways in unknown environments. This method analyzes the edges of a stairway based on planar motion tracking and directional filters. We extracted the horizontal edge of the stairs by using the Gabor Filter. From the specified set of horizontal line segments, we extracted a hypothetical set of targets by using the correlation method. Finally, we are proposing the use of the discrimination method to find the ground plane, using the behavioral distance measurement. Consequently, the remaining information is considered as an indoor stairway candidate region. After the stairway candidate region was obtained by applying our approach mentioned in the previous step, we proceeded with the candidate assessment tracking, based on the criterion of the minimum displaced frame difference, ground truth, as well as the rigidity of the stair. As a result, testing was able to prove its effectiveness

Outdoor stairway segmentation using vertical vanishing point and directional filter

In this paper we propose to detect the localization and recognition of outdoor stairway. This problem is the most fundamental step in solving the problem of autonomous stair climbing navigation. An autonomous system must be able to recognize parameters that can describe stairways in unknown environments. First, we proposed to extract the longest segments of diagonal lines from the edge image in order to identify a set of diagonal line segments candidate. This can provide information itself. Based on the vanishing point we defined the area where the stair candidate is located. We then applied the Gabor filter to detect the horizontal line. Finally, after combining the previous two steps, the algorithm defined the candidate stair area in the image. A set of stair images were used within a variety of conditions in our proposed method. As a result, testing was able to prove its effectiveness.In this paper we propose to detect the localization and recognition of outdoor stairway. This problem is the most fundamental step in solving the problem of autonomous stair climbing navigation. An autonomous system must be able to recognize parameters that can describe stairways in unknown environments. First, we proposed to extract the longest segments of diagonal lines from the edge image in order to identify a set of diagonal line segments candidate. This can provide information itself. Based on the vanishing point we defined the area where the stair candidate is located. We then applied the Gabor filter to detect the horizontal line. Finally, after combining the previous two steps, the algorithm defined the candidate stair area in the image. A set of stair images were used within a variety of conditions in our proposed method. As a result, testing was able to prove its effectiveness

Robust lane marking detection based on multi-feature fusion

In the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly, a lane marking clustering method is introduced. This is done by combining the edge and color information of the lane marking. Finally, a fitting model is implemented. A line fitting model is used to extract the lane marking parameters. However for those regions in which lane can not described as a line, the algorithm computed the curve parameters using Lagrange interpolating polynomial.In the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly, a lane marking clustering method is introduced. This is done by combining the edge and color information of the lane marking. Finally, a fitting model is implemented. A line fitting model is used to extract the lane marking parameters. However for those regions in which lane can not described as a line, the algorithm computed the curve parameters using Lagrange interpolating polynomial

Smoke detection for static cameras

This paper describes the smoke detection for static cameras. The background subtraction was used to determine moving objects. Color characteristics were utilized to distinguish smoke regions and other scene members. Separate pixels were united into blobs by morphology operations and connected components labeling methods. The image is then refined by boundary roughness and edge density to decrease amount of false detections. Results of the current frame are compared to the previous one in order to check the behavior of objects in time domain.This paper describes the smoke detection for static cameras. The background subtraction was used to determine moving objects. Color characteristics were utilized to distinguish smoke regions and other scene members. Separate pixels were united into blobs by morphology operations and connected components labeling methods. The image is then refined by boundary roughness and edge density to decrease amount of false detections. Results of the current frame are compared to the previous one in order to check the behavior of objects in time domain