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Existing approaches to object detection address the generation of object hypotheses by extracting several cues in natural and automotive images, relying on objects with sufficiently high resolution. Very little to almost no approaches, however, address the generation of hypothesis of very small or distant objects in images such as on motorways. Here, we propose a simple yet effective approach to generating...
We address the important problem of achieving robust and easy-to-deploy visual state estimation for micro aerial vehicles (MAVs) operating in complex environments. We use a sensor suite consisting of multiple cameras and an IMU to maximize perceptual awareness of the surroundings and provide sufficient redundancy against sensor failures. Our approach starts with an online initialization procedure...
Localization is a key capability for autonomous vehicles especially in urban scenarios. We propose the use of pole-like landmarks as primary features in these environments, as they are distinct, long-term stable and can be detected reliably with a stereo camera system. Furthermore, the resulting map representation is memory efficient, allowing for easy storage and on-line updates. The localization...
We develop a robust, unsupervised vehicle tracking system for videos of very congested road intersections in urban environments. Raw tracklets from the standard Kanade-Lucas-Tomasi tracking algorithm are treated as sample points and grouped to form different vehicle candidates. Each tracklet is described by multiple features including position, velocity, and a foreground score derived from robust...
In this paper, we propose a vision-based traffic light and arrow detection algorithm for intelligent vehicles. We detect all three traffic light colours along with the arrow direction robustly for varying illuminations and traffic lights. A fine-tuned convolutional neural network is used in an offline phase to localise the traffic light region-of-interest within a given camera image. Given the constrained...
An estimation framework is presented that improves the robustness of GPS-denied state estimation to changing environmental conditions by fusing updates from multiple view-based odometry algorithms. This allows the vehicle to utilize a suite of complementary exteroceptive sensors or sensing modalities. By estimating the vehicle states relative to a local coordinate frame collocated with an odometry...
Nowadays, the technological and scientific research related to underwater perception is focused in developing more cost-effective tools to support activities related with the inspection, search and rescue of wreckages and site exploration: devices with higher autonomy, endurance and capabilities. Currently, specific tasks are already carried out by remotely-operated vehicles (ROV) and autonomous underwater...
This contribution describes a system for accurate, robust and fast six degrees-of-freedom object pose estimation based on multi-feature models and a recursive filtering approach in the context of autonomous vehicle recharging. Feature measurements are integrated sequentially to allow full control over the feature detection algorithms and the influence on the estimate. This makes the system able to...
The increasing demand for urban mobility calls for a robust real-time traffic monitoring system. In this paper we present a vision-based approach for road traffic density estimation which forms the fundamental building block of traffic monitoring systems. Existing techniques based on vehicle counting and tracking suffer from low accuracy due to sensitivity to illumination changes, occlusions, congestions...
This paper presents a large-scale evaluation of a visual localisation method in a challenging city environment. Our system makes use of a map built by combining data from LIDAR and cameras mounted on a survey vehicle to build a dense appearance prior of the environment. We then localise by minimising the normalised information distance (NID) between a live camera image and an image generated from...
This paper introduces the method of detecting a route space for an industrial indoor vehicle. The vehicles work in many industrial fields, e.g., a semiconductor production and a car assembly factory. The detection of the route space under illuminant disturbance is an important problem for the industrial vehicle robot. The industrial vehicle has to move to the same areas in a factory. For these works,...
This article is about vehicles detection from visual data which is an important part of automotive driving assistance systems. It is a big challenge to make the vehicles detection more robust. In order to enhance the robustness, a lane lines stabilization methods is proposed by studying the imaging model. Next, a hypothesis generation and verification framework is applied for saving time. Finally,...
Extrinsic camera parameters estimation is an important task for many assistance systems. The reconstruction of the image to world projection depends strongly on robust estimated camera parameters. Based on the used camera system, the extrinsic parameters estimation can be a complicated task, that maybe requires a lot of computing capacity. In this paper, an extrinsic camera parameters estimation procedure...
Detecting an abandoned object in crowded scenes of surveillance videos becomes more complex task due to occlusions, lighting changes, and other factors. In this paper, a new framework to detect abandoned object using dual background model subtraction is presented. In our system, the adaptive background model is generated based on statistical information of pixel intensity that robust against lighting...
This paper describes an illumination and pose invariant face recognition system that is intended to be used in the automotive market for vehicle personalization. Near-infrared frame differencing improves the robustness to the outdoor illumination conditions. And we introduce the video-based recognition with pose clustering for pose invariant face recognition. We have collected large video dataset...
In this paper, we present a solution to generate semantically richer descriptions and instructions for driver assistance and safety. Our solution builds upon a set of computer vision and machine learning modules. We start with low-level image processing and finally generate high-level descriptions. We do this by combining the results of the image pattern recognition module with the prior knowledge...
A new robust lane marking detection algorithm for monocular vision is proposed. It is designed for the urban roads with disturbances and with the weak lane markings. The primary contribution of the paper is that it supplies a robust adaptive method of image segmentation, which employs jointly prior knowledge, statistical information and the special geometrical features of lane markings in the bird's-eye...
Driving assistance system has a significant influence on driving safety, and we introduce an integrated Forward Collision Warning(FCW) system based on monocular vision. In order to reduce the searching region of original image, lane making is presented to establish the ROI firstly. Secondly, hypotheses are extracted using Haar-like feature and Adaboost classifier. To remove false positive detection...
In this paper, we propose a robust curved lane marking detection method by first detecting a straight lane and applying a geometric model of that detected straight lane. In our proposed method, we first detect the straight line and generate 13 candidates of the curved lane by applying a geometric model. We then vote those candidates on the feature image and consider the candidate which acquires the...
Lateral localization of an autonomous vehicle within its lane is major information for its adequate control and navigation. Computer vision and robotics communities have used primarily images to Bird's Eye View for easier data manipulation than perspective image. Nevertheless, this technique usually assumes that the terrain is flat and needs calibration for its transformation matrix. In this paper...
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