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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver 3D absolute gaze point obtained through the combined use of a front-view...
This paper presents a vehicle navigation system that is capable of achieving sub-meter GPS-denied navigation accuracy in large-scale urban environments, using pre-mapped visual landmarks. Our navigation system tightly couples IMU data with local feature track measurements, and fuses each observation of a pre-mapped visual landmark as a single global measurement. This approach propagates precise 3D...
In recent years, location-based services and indoor positioning systems gained increasing importance for both, research and industry. Visual localization systems have the advantage of not being dependent on dedicated infrastructure and thus are especially interesting for navigation within buildings. While there are already approaches of using pre-recorded databases of reference images to obtain an...
The percentage of false alarms caused by spiders in automated surveillance can range from 20–50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by...
Accurate detection and localization of vehicles in aerial images has a wide range of applications including urban planning, military reconnaissance, visual surveillance, and realtime traffic management. Automated detection of vehicles in aerial imagery is a challenging task, due to the density of vehicles on the road, the complexity of the surrounding environment in urban areas, and low spatial resolution...
Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen...
This paper presents a visual attention based convolutional neural network (CNN) to solve the image classification problem in the real complex world scene. The presented method can simulate the process of recognizing objects and find the area of interest which is related with the task. Compared with the CNN method in image classification, the model is proficient in fine-grained classification problem...
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...
Fatigue during long-time driving threatens the safety of drivers and transportation. In this paper, we provide an effective method based on multi-sensor signals collected from Kinect2.0 camera and PPG pulse sensor to build a driver fatigue detection system. Unlike most traditional works, we define the transitional process of fatigue and elaborate its effect on training classifiers. The simulation...
Vehicle ego-localization is a critical task not only for in-car navigation systems, but also for emerging intelligent and autonomous vehicle technologies. Visual localization methods that determine current location by performing image matching against a pre-constructed database have an accuracy limited by the spatial distance between database images. In this paper we propose a method that uses the...
Automation techniques have been applied in almost every field in past few years. Automated Guided Vehicle (AGV) are most often used in industries and inventories for object management. Obstacle avoidance being a necessary requirement for navigation in any vehicle, still faces many challenges in the field of automation due to uncertain nature of the surrounding environment. This paper presents the...
Traffic density estimation plays an integral role in intelligent transportation systems (ITS), using which provides important information for signal control and effective traffic management. In this paper, we present a new framework for traffic density estimation based on topic model, which is an unsupervised model. This framework uses a set of visual features without any need to individual vehicle...
We take a look at current state of traffic sign classification discussing what makes it a specific problem of visual object classification. With impressive state-of-the-art results it is easy to forget that the domain extends beyond annotated datasets and overlook the problems that must be faced before we can start training classifiers. We discuss such problems, give an overview of previous work done,...
Drowsy driving is considered one of the most serious causes of fatal traffic accidents, especially for long distance drivers who struggle significantly during monotonous driving conditions. As a result, various types of warning systems have been proposed among other preventive measure against drowsy driving. These systems issue a warning upon detecting a decline in driving performance as indicated...
Traditional vehicle recognition and retrieval systems are almost based on vehicle license plate recognition, which requires the user to provide images containing the license plate of the vehicle. In order to recognize and retrieve vehicles more convenient and efficient, and solve the problem of missing or wrong license plate, this paper proposes a method based on the overall appearance characteristics...
In this paper, a simple approach for estimating the ego-motion of a vehicle is proposed. The system can estimate the motion based on a sequence of images and point cloud data. The system is capable of estimate the ego-motion through a 2-D to 3-D mapping of the detected features on the image considering that the calibration parameters of the sensors are available. Singular value decomposition is used...
Rotary-wing unmanned aerial vehicles (UAV) are being widely used in different applications due to its several features, such as mobility, lightweight, embedded processing and capability of flying in different height levels. Among the possible applications they are used in surveillance tasks, agriculture environments monitoring, power lines inspections and diseases detection in crops. The images captured...
Among the human related factors, aggressive driving behavior is one of the major causes of traffic accidents [17]. On the other hand, detection and characterization of driver aggressiveness is a challenging task since there exist different psychological causes behind it. However, information about the driver behavior could be extracted from the data that is collected via different sensing devices...
This paper considers visual feature selection and its regression to estimate the position of a vehicle using an omnidirectional camera. The Gaussian process (GP)-based localization builds on a maximum likelihood estimation (MLE) with a GP regression from optimally selected visual features. In particular, the collection of selected features over a surveillance region is modeled by a multivariate GP...
In this paper, we proposes a visual-based vehicle classification system, in which it involves visual feature representation and classification step. In the feature representation step, we present a center enhanced spatial pyramid matching (CE-SPM) to extract the feature from images. In this work, we defined additional region in the center of each images to calculate the histograms of visual words...
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