We present a real-time algorithm for simultaneous localization and local mapping (local SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with a laser scanner, short range radars and odometry. To correct the vehicle odometry we introduce a new fast implementation of incremental scan matching method that can work reliably i...

Vehicle detection technology is the key technology of intelligent transportation systems, attracting the attention of many researchers. Although much literature has been published concerning daytime vehicle detection, little has been published concerning nighttime vehicle detection. In this study, a nighttime vehicle detection algorithm, consisting of headlight segmentation, headlight pairing a...

The moth-inspired plume-tracing strategies on a single REMUS underwater vehicle successfully tracked a Rhodamine dye plume over 100 m and declared its source location in near-shore ocean environments that are characterized by turbulence, tides, and waves. This paper expands moth-inspired plume tracing via a single vehicle to multiple vehicles. The new strategy includes a mechanism determining a...

The focus of this research was automatic visual tracking of realistic underwater objects. Powerful real-time ltering and correlation hardware was used to generate fullimage range and velocity maps at frame rates. These maps were in turn processed to provide object centroid position and rate. The object tracking system was tested experimentally on an underwater robotic vehicle, and qualitative r...

This paper describes a real-time vision-based system that detects vehicles approaching from the rear in order to anticipate possible rear-end collisions. A camera mounted on the rear of the vehicle provides images which are analysed by means of computer vision techniques. The detection of candidates is carried out using the top-hat transform in combination with intensity and edge-based symmetri...

A statistical and computer vision approach using tracked moving vehicle shapes for auto-calibrating traffic surveillance cameras is presented. Various methods have been designed to estimate scene vanishing point and invertible transformation between real world and image coordinates. Results are validated against traditional methods in different traffic locations yield accurate results with much...

Increasing congestion on freeways and problems associated with existing detectors have spawned an interest in new vehicle detection technologies such as video image processing. Existing commercial image processing systems work well in free-flowing traffic, but the systems have difficulties with congestion, shadows and lighting transitions. These problems stem from vehicles partially occluding o...

Increasing congestion on freeways and problems associated with existing detectors have spawned an interest in new vehicle detection technologies such as video image processing. Existing commercial image processing systems work well in freēowing trac, but the systems have diculties with congestion, shadows and lighting transitions. These problems stem from vehicles partially occluding one anot...

Odometry is widely used to localize wheeled and tracked vehicles because of its simplicity and continuity. Odometric calculations integrate the wheel or track’s rotation speed. The accuracy of position thus calculated, is affected by slippage between the ground and the wheel or track. When traveling on a loose slope, the localization accuracy of the odometry decreases remarkably due to slippage...

In order to estimate the speed of a moving vehicle with side view camera images, velocity vectors of a sufficient number of reference points identified on the vehicle must be found using frame images. This procedure involves two main steps. In the first step, a sufficient number of points from the vehicle is selected, and these points must be accurately tracked on at least two successive video ...