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In this paper we focus on the problem of pedestrian detection in low visibility conditions, with infrared cameras. Widely applied, tracking is essential for driving assistance applications, providing support for removing false positives and forcing the detection of border line true positives. We propose a multiple feature and temporal based pedestrian detector for far-infrared images. Our model benefits...
Scene flow estimation jointly recovers dense scene structure and motion from at least two pairs of stereo images, thus generalizing classical disparity and optical flow estimation. Such a complete description of the scene has many uses in the field of automated driving such as dynamic traffic object detection or infrastructure element detection. Estimation of the structure and motion of each scene...
Depth estimation of the surrounding environment using a stereoscopic camera setup is an important and fundamental research topic in computer vision. Due to its running time and quality performance in real situations the semi global matching algorithm is often used. The biggest disadvantage of the semi global approach is its large memory footprint. On the other hand, block matching stereo is leaner...
An important cause of road accidents is the reduced visibility due to the presence of fog or haze. For this reason, there is a fundamental need for Advanced Driving Assistance Systems (ADAS) based on efficient real time algorithms able to detect the presence of fog, estimate the fog's density, determine the visibility distance and inform the driver about the maximum speed that the vehicle should be...
The main issues with classic disparity estimation methods are the limitations of cost fitting for estimating subpixel disparity values and the frequent violations of the fronto-parallel assumption during support window matching. Modern stereo correspondence algorithms model the scene as a collection of 3D planes and estimate the real-valued parameters of each plane in order to obtain a more accurate...
Visual odometry is the most suitable method for recovering the camera motion in the context of video processing applications. The main advantages it brings are the accuracy of the estimation, the computation efficiency, and the elimination of the need to synchronize a video processing system with other odometry sensors. There is a large amount of recently published visual odometry methods, but none...
The paper presents a method for automatically detecting pallets and estimating their position and orientation. For detection we use a sliding window approach with efficient candidate generation, fast integral features and a boosted classifier. Specific information regarding the detection task such as region of interest, pallet dimensions and pallet structure can be used to speed up and validate the...
This paper addresses the problem of finding the host vehicle's lateral position on a multi-lane road, using information obtained by processing video sequences. A very important cue for lane identification is the class of the boundaries of the current lane. This paper presents a reliable solution for lane boundary type identification, based on frequency analysis of the gray level profile of these boundaries,...
Reliable vehicle ego motion estimation based on visual information is an important research goal because it has applications like accurate long term localization by fusion with other sensors, temporal fusion between frames, moving obstacles detection and tracking, path planning etc. This paper evaluates and significantly improves some steps of existing visual odometry methods. The main contribution...
Modeling and tracking dynamic entities in the driving environment is a complex task, as one has to accommodate multiple types of scenarios. Extraction of dynamic properties of obstacles becomes difficult when the measurement sensors do not provide speed directly. The dynamic polyline representation of obstacles is a compromise between the rigid model-based cuboid representation and the model-free...
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