Multi-Resolution Vehicle Detection using Artificial Vision

This paper describes a vehicle detection system using a single camera. It is base

The Single Frame Stereo Vision System for Reliable Obstacle Detection used during the 2005 DARPA Grand Challenge on TerraMax

Autonomous driving in off-road environments requires an exceptionally capable sensor system, especially given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a variable-width-baseline (up to 1.5 meters) single-frame stereo vision system for obstacle detection that can meet the needs of autonomous navigation in extreme environments. Efforts to maximize computational speed ---both in the attention given to accurate and stable calibration and the exploitation of the processors MMX and SSE instruction sets--- allow a guaranteed 15 fps rate. Along with the assured speed, the system proves very robust against false positives. The system has been field tested on the TerraMax vehicle, one of only five vehicles to complete the 2005 DARPA Grand Challenge course and the only one to do so using a vision system for obstacle detection

Fedriga, “A Decision Network Based Frame-work for Visual Off-Road Path Detection Problem

Abstract — This paper describes a Decision Network based frame-work used for path-detection algorithm development in autonomous vehicle applications. Lane marker detection algorithms do not work in off-road environments. Off-road trails have too much complexity, with widely varying textures and many differing natural boundaries. The authors have developed a general approach. Images are segmented into regions, based on the homogeneity of some pixel properties and the resulting regions are classified as road or not-road by a Decision Network Process. Combinations of contiguous clusters form the path surface, allowing any arbitrary path to be represented. I

Obstacle Detection with Stereo Vision for Off-Road Vehicle Navigation

In this paper we present an artificial vision algorithm for real-time obstacle detection in unstructured environments. The images have been taken using a stereoscopical vision system. The system uses a new approach, of low computational load, to calculate a V-disparity image between left and right corresponding images, in order to estimate the cameras pitch oscillation caused by the vehicle movement. Then, the obstacles are localized by stereo matching and mapped in real world coordinates. Experimental results on sequences taken from a moving vehicle (which partecipated to the DARPA Grand Challenge 2004) in different unstructured scenarios are then presented, to demonstrate the validity of the approach

The Single Frame Stereo Vision System for Reliable Obstacle Detection used during

Abstract — Autonomous driving in off-road environments requires an exceptionally capable sensor system, especially given that the unstructured environment does not provide many of the cues available in on-road environments. This paper presents a variable-width-baseline (up to 1.5 meters) single-frame stereo vision system for obstacle detection that can meet the needs of autonomous navigation in extreme environments. Efforts to maximize computational speed —both in the attention given to accurate and stable calibration and the exploitation of the processors MMX and SSE instruction sets — allow a guaranteed 15 fps rate. Along with the assured speed, the system proves very robust against false positives. The system has been field tested on the TerraMax TM vehicle, one of only five vehicles to complete the 2005 DARPA Grand Challenge course and the only one to do so using a vision system for obstacle detection. I

Obstacle detection with stereo vision for off-road vehicle navigation. Paper presented at the

In this paper we present an artificial vision algorithm for real-time obstacle detection in unstructured environments. The images have been taken using a stereoscopical vision system. The system uses a new approach, of low computational load, to calculate a V-disparity image between left and right corresponding images, in order to estimate the cameras pitch oscillation caused by the vehicle movement. Then, the obstacles are localized by stereo matching and mapped in real world coordinates. Experimental results on sequences taken from a moving vehicle (which partecipated to the DARPA Grand Challenge 2004) in different unstructured scenarios are then presented, to demonstrate the validity of the approach. 1

Vehicle Detection and Localization in Infra-Red Images

This paper presents an algorithm for detecting vehicles in FIX images. Initially the attention is focused on portions of the image that contains hot objects only. These areas are then selected and refined using aspect ratio and size constraints about vehicles; even situations with overlapping vehicles are considered. The result Is further investigated exploiting specific vehicle thermal characteristics. A simple tracking phase is performed to improve the detection results. Thanks to the knowledge of camera intrinsic parameters the distance of vehicles is computed using an assumption about vehicles width. The system proved to be effective in different scenarios, but further tests are required to validate it in a wider range of weather conditions. It is able detect vehicles in front of the vision system in the range 25 m-100 m at a 12 Hz processing rate

Pedestrian Detection by means of Far-infrared Stereo Vision

This article presents a stereo system for the detection of pedestrians using far-infrared cameras. Since pedestrian detection in far-infrared images can be difficult in some environmental conditions, the system exploits three different detection approaches: warm area detection, edge-based detection, and disparity computation. A final validation process is performed using head morphological and thermal characteristics. Currently, neither temporal correlation, nor motion cues are used in this processing. The developed system has been implemented on an experimental vehicle equipped with two infrared cameras and preliminarily tested in different situations