Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison between low-cost passive and active vision for obstacle depth

Obstacle detection is a key issue in many current applications, especially in applications that have been increasingly highlighted such as: advanced driver assistance systems (ADAS), simultaneous localization and mapping (SLAM) and autonomous navigation system. This can be achieved by active and passive acquisition vision systems, for example: laser and cameras respectively. In this paper we present a comparison between low-cost active and passive devices, more specifically LIDAR and two cameras. To this comparison a disparity map is created by stereo correspondence through two images and a point cloud map created by LIDAR data values (distances measures). The obtained results shown that passive vision can be as good as or even better than active vision in low cost scenarios

Comparison of Forward Vehicle Detection Using Haar-like features and Histograms of Oriented Gradients (HOG) Technique for Feature Extraction in Cascade Classifier

This paper present an algorithm development of vehicle detection system using image processing technique and comparison of the detection performance between two features extractor. The main focus is to implement the vehicle detection system using the on-board camera installed on host vehicle that records the moving road environment instead of using a static camera fixed in certain locations. In this paper, Cascade classifier is trained with image dataset of positive images and negative images. The positive images consist of rear area of the vehicle and negative image consist of road scene background. Two features extractor, Haar-like features and histograms of oriented gradients (HOG) are used for comparison in this system. The image dataset for training in both feature extractions are fixed in dimension. In comparison, the accuracy and execution time are studied based on its detection performance. Both features performed well in detection accuracy, whilst the results indicate that the Haar-like features execution time is 26% faster than by using HOG feature

Road Triangle Detection for Non-Road Area Elimination Using Lane Detection and Image Multiplication

The background has become the key issue in maintaining the accuracy of final analysis for object detection in the development of an image processing algorithm. Therefore, this paper focuses on intelligent transport system (ITS), in which some of the background characteristics such as trees, road divider, and buildings interfere in the detection system algorithm. Therefore, this paper presents an algorithm that can remove the unwanted background, outside the road area boundaries for dynamic video footage. Using the onboard camera to capture the road traffic, the background is always moving in motion together with the foreground; therefore, a region of interest that focuses only on the road region needs to be established. The algorithm consists of three main components: lane detection, vanishing point and image multiplication. From the three components, other methods are applied, namely Hough transform, line intersection, image masking and image multiplication, which are combined together to create the background subtraction system. In the final analysis, the test results under various road conditions show a good detection rate and background removal

A new approach to highway lane detection by using hough transform technique

This paper presents the development of a road lane detection algorithm using image processing techniques.This algorithm is developed based on dynamic videos, which are recorded using on-board cameras installed in vehicles for Malaysian highway conditions.The recorded videos are dynamic scenes of the background and the foreground, in which the detection of the objects, presence on the road area such as vehicles and road signs are more challenging caused by interference from background elements such as buildings, trees, road dividers and other related elements or objects. Thus, this algorithm aims to detect the road lanes for three significant parameter operations; vanishing point detection, road width measurements, and Region of Interest (ROI) of the road area, for detection purposes.The techniques used in the algorithm are image enhancement and edges extraction by Sobel filter, and the main technique for lane detection is a Hough Transform. The performance of the algorithm is tested and validated by using three videos of highway scenes in Malaysia with normal weather conditions, raining and a night-time scene, and an additional scene of a sunny rural road area. The video frame rate is 30fps with dimensions of 720p (1280x720) HD pixels. In the final achievement analysis, the test result shows a true positive rate, a TP lane detection average rate of 0.925 and the capability to be used in the final application implementation

Integrated Real-Time Vision-Based Preceding Vehicle Detection in Urban Roads

7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, 11-14 August 2011This paper presents a real-time algorithm for a vision-based preceding vehicle detection system. The algorithm contains two main components: vehicle detection with various vehicle features, and vehicle detection verification with dynamic tracking. Vehicle detection is achieved using vehicle shadow features to define a region of interest (ROI). After utilizing methods such as histogram equalization, ROI entropy and mean of edge image, the exact vehicle rear box is determined. In the vehicle tracking process, the predicted box is verified and updated. Test results demonstrate that the new system possesses good detection accuracy and can be implemented in real-time operation.Department of Civil and Environmental Engineerin