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Automobile navigation in tunnel environment is challenging. GPS sensors and ordinary cameras can't function effectively. For navigation, infrared cameras are installed on top of our experimental vehicle, and here we propose an efficient object detection method to detect emergency lights from the collected data in tunnel environment. The proposed method firstly detects keypoints by setting thresholds...
Due to the increase in road transportation several projects concerning automated highway systems were initiated to optimize highway capacity. In the future, the developed techniques should be applicable in unstructured environment (e.g. desert) and adaptable for heterogeneous vehicles. But before, several challenges, i.e. independency of lane markings, have to be overcome. Our solution is to consider...
Advanced warning system for vehicles is a critical issue in recent years for automobiles, especially when the number of vehicles is growing rapidly world wide. The cost down of general cameras makes it feasible to have an intelligent system of visual-based event detection in front for forward collision avoidance and mitigation. When driving at nighttime, vehicles in front are generally visible by...
This paper presents an occupant classification approach based on monocular vision for smart airbags that can decide to deploy or turn off intelligently. The main focus of this work different from those in the literature is on addressing the issue of the movement of car seat. The idea behind is to introduce the relation between the object of interest and scene inside the vehicle, namely, contextual...
Motion detection is a vital part of vision systems, either biological or computerized. Conventional motion detection methods in machine vision can differentiate moving objects from background, but cannot directly handle different types of motions. In this paper, we present Genetic Programming (GP) as a method which not only removes relatively stationary background, but also can be selective on what...
This paper addresses the problem of using visual information to estimate vehicle motion (a.k.a. visual odometry) from a machine learning perspective. The vast majority of current visual odometry algorithms are heavily based on geometry, using a calibrated camera model to recover relative translation (up to scale) and rotation by tracking image features over time. Our method eliminates the need for...
We present a novel approach for vehicle detection in urban surveillance videos, capable of handling unstructured and crowded environments with large occlusions, different vehicle shapes, and environmental conditions such as lighting changes, rain, shadows, and reflections. This is achieved with virtually no manual labeling efforts. The system runs quite efficiently at an average of 66Hz on a conventional...
In this paper, we introduce the concept of personal driving diary. A personal driving diary is a multimedia archive of a person's daily driving experience, describing important driving events of the user with annotated videos. This paper presents an automated system that constructs such multimedia diary by analyzing videos obtained from a vehicle-mounted camera. The proposed system recognizes important...
This paper investigates the feasibility of classifying winter road surface conditions using images from low cost cameras mounted on regular vehicles. RGB features along with gradients have been used as feature vectors. A Support Vector Machine (SVM) is trained using the extracted features and then used to classify the images into their respective categories. Different training schemes and their effect...
In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from largeamounts of unlabeled data. For that purpose, we proposea novel on-line co-training method based on visual and acoustic information. Our system does not need complicated microphone arrays or video calibration and automatically adapts to specific traffic scenes. These specialized detectors...
The most prevailing approach now for parking lot vehicle detection system is to use sensor-based techniques such as ultrasound and infrared-light sensors. A few engineering firms provide camera-based systems, which are only for underground and indoor parking lots due to the poor accuracy of the detector. The main impediments to the camera-based system in applying to outdoor parking lots are adherent...
Detecting moving objects is a significant component in many machine vision systems. One of the challenges in real world motion detection is the unstability of the background. An ideal method is expected to reliably detect interesting movements from videos while ignoring background/uninteresting movements. In this paper, Genetic Programming (GP) based motion detection method is used to tackle this...
A learning system for detection and classification of road obstacles, such as vehicles and non-vehicles, is proposed which utilizes information from multiple sensors. An advanced range sensor guides a selection of candidate images provided by the camera for subsequent analysis. A competition based learning algorithm is used to distinguish between representations of different obstacles. High classification...
Space invariant object recognition is one of the difficult problems of pattern recognition and has many potential applications. In this paper, a vehicle recognition system is proposed for toll plaza monitoring and auditing. This system recognizes the type of approaching vehicle such as truck, bus, car etc. irrespective of geometrical distortion of vehicles such as scale and rotation. Maximum Average...
Awareness to a vehicle's surrounding is necessary for safe driving. Current surround technologies focus on the detection of obstacles in hard-to-view places but may neglect temporal information. This paper seeks the causes of dangerous situations by examining surround behavior. A general hierarchical learning framework is introduced to automatically learn surround behaviors. By observing motion trajectories...
A machine learning approach is presented in this study to automatically construct motion detection programs. These programs are generated by genetic programming (GP), an evolutionary algorithm. They detect motion of interest from noisy data when there is no prior knowledge of the noise. Programs can also be trained with noisy data to handle noise of higher levels. Furthermore, these auto-generated...
In many driver assistance systems and autonomous driving applications, both LIDAR and computer vision (CV) sensors are often used to detect vehicles. LIDAR provides excellent range information to different objects. However, it is difficult to recognize these objects as vehicles from range information alone. On the other hand, computer vision imagery allows for better recognition, but does not provide...
Urban traffic surveillance, which is designed to improve traffic management, is an important part of intelligent traffic system (ITS). In particular, airborne moving vehicle detection has become a new but hot research area since its wide view and low cost. However, airborne urban traffic surveillance is impacted by many difficulties such as camera vibration, vehicle congestion, background variance,...
Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based object classification methods tend to fail under these situations. We approach the problem by designing an adaptive object classification framework which automatically adjust to different...
This paper describes the procedure for detection and tracking of a vehicle from an on-road image sequence taken by a monocular video capturing device in real time. The main objective of such a visual tracking system is to closely follow objects in each frame of a video stream, such that the object position as well as other geometric information are always known. In the tracking system described, the...
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