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This paper proposes a novel approach of developing a vision-based forward collision warning system (V-FCW) under an integrated platform with both V-FCW algorithm development and field-test data driven system verification. The developed verification platform provides huge amount of video data collected from field testing under various real driving conditions with labeled ground truth and fast search...
This paper studies a sensor fusion method focusing on a collision avoidance system, capable of high accurate object detection by blending a camera and 2D LIDAR with the aid of pixel analysis. Pixel analysis is performed via single camera under a concern that the free space in front of vehicles is limited by objects on almost vertical surfaces. The considered problem is defined as that how to efficiently...
Vehicle and Pedestrian Detection is a key problem in computer vision, with several applications including robotics, surveillance and automotive safety. Much of the progress of the past few years has been driven by the availability of challenging public datasets. In this paper, we build up a vehicle and pedestrian detection system by combing Histogram of Oriented Gradients (HoG) feature and support...
Vehicles detection and classification are the most popular subjects in the computer vision researching field, and also are the most important parts in any traffic monitoring or surveillance system. Although there has been a considerable amount of ideas to accommodate this problem since the 90s, many problems are still unresolved due to the complexity of traffic systems and the variety of vehicles...
Deep convolutional Neural Networks (DNN) is the state-of-the-art machine learning method. It has been used in many recognition tasks including handwritten digits, Chinese words and traffic signs, etc. However, training and test DNN are time-consuming tasks. In practical vehicle detection application, both speed and accuracy are required. So increasing the speeds of DNN while keeping its high accuracy...
This paper proposes an event detection method using noisy object information. Some events have a close connection with objects, and the objects related to the event often appear with the event in a video. For example, if an event "Grooming an animal" appears in a video, an animal and people should appear in the video. If we detect the objects that have a close connection with the events,...
This paper presents a moving object tracking system with a Particle Filter algorithm. A software tool is developed to track an unknown moving object in a sensing region occupied by other dynamic objects. Several components are used to determine objects, to self-localize, and to match the determined objects iteratively in conjunction with the previously determined objects. Each object is labeled with...
This paper proposes a vehicle detection and traffic volume statistics algorithm Based on an improved single Gaussian model. This algorithm contains three major sections, which are moving target detection, shadows suppression and traffic volume count. Firstly, with an improved background initialization method, the paper detects the moving target by using the single Gaussian model. Then, a computational...
In this paper, an improved implicit shape model is presented for on-road vehicle and pedestrian detection. Implicit shape model (ISM) is widely used for object detection and categorization. The training of ISM usually consists of three components: interest point detector, local feature descriptor, codebook generation. We evaluate six common interest point detectors to determine the best detector for...
Unmanned aerial vehicles (UAV) are among the fast growing remote sensing technologies in these last few years. This is mainly because UAVs allow acquiring images characterized by an extremely high spatial resolution and they exhibit an interesting operational flexibility. Taking advantage from these unique characteristics can help in addressing problems typical of the civilian contexts. In particular,...
This paper reviews current approaches, challenges, objectives, architecture and trends in the perception, one of the most important systems in autonomous ground vehicles AGV. Several implementations reported in the literature in the past five years are reviewed to see the evolution and to determine the main areas for future research.
Autonomous vehicles are increasingly used for transportation of supply and goods. This is done mainly indoors. In outdoor scenarios, a reliable vision system is crucial for the overall system performance. The restriction of the reliability of this vision system is caused by light changes. To overcome the problem of varying lightning conditions, thermal infra-red cameras are often used. This paper...
We study the use of domain adaptation and transfer learning techniques as part of a framework for adaptive object detection. Unlike recent applications of domain adaptation work in computer vision, which generally focus on image classification, we explore the problem of extreme class imbalance present when performing domain adaptation for object detection. The main difficulty caused by this imbalance...
Object detection is an important and challenging problem in the field of computer vision. Classical object detection approaches such as background subtraction and saliency detection do not require manual collection of training samples, but can be easily affected by noise factors, such as luminance changes and cluttered background. On the other hand, supervised learning based approaches such as Boosting...
Target Detection involves the task of identifying and zeroing in on those set of pixels of an image that contain the required information (target). It has potential applications in diverse fields including automatic surveillance of large areas, illegal vehicle movement tracking in remote areas etc. This technique poses many challenges in terms of retaining only the target pixels by identifying and...
Advanced Driver Assistance Systems (ADAS) are used for assisting the drivers by providing advice and warnings when necessary. CTA (Cross Traffic Alert) systems are a subset of ADAS used for detecting objects (viz., cars, trucks, pedestrians, static objects etc) by using one or more moving cameras, mounted on a vehicle. Usually, CTA systems can detect moving objects within region of interest (ROI)...
In this paper, an improved intersection detection method is proposed by applying the VCS-based algorithm on the registered scans instead of the single scan. Both the registration and intersection detection approach are independent on Global Positioning System (GPS), Geographic Information System (GIS), Inertial Navigation System (INS) or other auxiliaries which have been extensively used in autonomous...
In Advanced Driver Assistance Systems (ADAS), object tracking is a crucial method to foresee dangerous situations. The Joint Integrated Probabilistic Data Association (JIPDA) offers the advantage, that existence and association uncertainties are considered in multi-target tracking. Recent- ly, real-time capable implementations have been presented. However, the real-time capability is only given, if...
Recently, the introduction of dense, long-range 3D sensors has facilitated tracking of arbitrary objects. Especially in the context of autonomous driving, other traffic participants driving the streets usually stay well-segmented from each other. In contrast, pedestrians or bicyclists do not always stay on the road and they often get close to static structure of the environment, e.g. traffic lights...
Moving objects detection and recognition around an intelligent vehicle are active research fields. A great number of approaches have been proposed in recent decades. This paper proposes a novel approach based solely on spatial information to solve this problem. Moving objects detection is achieved in conjunction with an egomotion estimation by sparse matched feature points. For objects recognition,...
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