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Visually estimating a robot's own motion has been an active field of research within the last years. Though impressive results have been reported, some application areas still exhibit huge challenges. Especially for car-like robots in urban environments even the most robust estimation techniques fail due to a vast portion of independently moving objects. Hence, we move one step further and propose...
In this paper we propose an approach for dynamic scene perception from a moving vehicle equipped with a stereo camera rig. The approach is solely based on visual information, hence it is applicable to a large class of autonomous robots working in indoor as well as in outdoor environments. The proposed approach consists of an egomotion estimation based on disparity and optical flow using the Longuet-Higgins-Equations...
An advanced method for road course estimation is presented. It is based on the state-of-the-art Kalman filter lane detection and allows for a robust sensor-based estimation of road courses in great distances. Only the parameters for the road course are estimated which results in a reduced parameter space and therewith more robustness. Instead of laterally displaced single feature points tangential...
In this paper, we propose a vehicle detection and tracking algorithm. The detection is done using the median filtering and blob extraction. Median filtering is used for background extraction which is later subtracted from the motion frames for object detection. Morphological operators are employed for blob extraction. Hence, object detection is achieved using median filtering and morphological closing...
In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art lane tracking systems are not capable of performing...
A target selection method based on multi features fusion is proposed to improve the accuracy of target vehicle selection. The parameters consisting of the longitudinal distance, lateral distance, relative speed between objects and the host vehicle, the in-lane probability of objects are regarded as the features of individual vehicles. Firstly, some pre-processes of features data are carried out including...
This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most...
As traffic surveillance technologies continue to grow worldwide, vehicle detection, counting and tracking are becoming increasing important. This paper proposes a real-time multi-vehicle detection and tracking approach. Lane marker detection is carried out for vehicle counting on each lane. It also helps remove the foreground noise and shadow. Instead of tracking the entire vehicle blob, vehicle sub-feature...
Shape matching is one of the most popular methods for recognition and tracking of moving objects in video sequence. When the method is used for recognizing the moving & rotated object, it is limited for practical use due to consuming a lot of calculation time. Aiming at the problem, a real-time tracking method of moving objects based on Kalman filtering and Gabor Decomposition is proposed. First...
In recent years, feature based object detection has attracted increasing attention in computer vision research community. However, to our best knowledge, no previous work has focused on utilizing local binary pattern (LBP) for vehicle detection in intelligent transportation system (ITS) domain. In this paper, we develop a novel traffic monitoring system based on N-LBP algorithm, which is the new LBP...
In this work a learning algorithm for visual object tracking is presented. As object representation a fast computable set of Haar-like features is used and a weighted correlation is applied for the matching process. The object tracker utilizes the same set of features that is already calculated for object detection and thus it is possible to reuse features for detection and tracking. The feature's...
Aiming at vehicle detection and tracking problems in video monitoring and controlling system, this paper mainly studies vehicle detection and tracking problems in conditions of high traffic density in daytime. This paper is distinguished by two key contributions. First, we develop an improvement - SEAP (Simple but Efficient After Process) which checks the detection results in an accurate way and is...
In recent years, the Viola and Jones rapid object detection approach became very popular. One aspect why this approach achieved acceptance is the numerical efficient computation of the Haar-like features on basis of the integral image. This efficiency is essential for sliding window techniques, where features have to be extracted for huge amounts of data. The main contribution of this paper is an...
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