Vehicle Detection

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Ronald Miller - One of the best experts on this subject based on the ideXlab platform.

  • On-road Vehicle Detection: A review
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
    Co-Authors: Zehang Sun, George Bebis, Ronald Miller
    Abstract:

    Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other Vehicles has attracted a lot of attention lately. In these systems, robust and reliable Vehicle Detection is a critical step. This paper presents a review of recent vision-based on-road Vehicle Detection systems. Our focus is on systems where the camera is mounted on the Vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road Vehicle Detection using optical sensors followed by a brief review of intelligent Vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based Vehicle Detection. Methods aiming to quickly hypothesize the location of Vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating Detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for Vehicle Detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

George Bebis - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Vehicle Detection from aerial imagery
    2011 IEEE International Conference on Robotics and Automation, 2011
    Co-Authors: Joshua Gleason, Ara V. Nefian, Xavier Bouyssounousse, Terry Fong, George Bebis
    Abstract:

    Vehicle Detection from aerial images is becoming an increasingly important research topic in surveillance, traffic monitoring and military applications. The system described in this paper focuses on Vehicle Detection in rural environments and its applications to oil and gas pipeline threat Detection. Automatic Vehicle Detection by unmanned aerial Vehicles (UAV) will replace current pipeline patrol services that rely on pilot visual inspection of the pipeline from low altitude high risk flights that are often restricted by weather conditions. Our research compares a set of feature extraction methods applied for this specific task and four classification techniques. The best system achieves an average 85% Vehicle Detection rate and 1800 false alarms per flight hour over a large variety of areas including vegetation, rural roads and buildings, lakes and rivers collected during several day time illuminations and seasonal changes over one year

  • Decision-level fusion for Vehicle Detection
    2007
    Co-Authors: Zehang Sun, George Bebis, Nikolaos G. Bourbakis
    Abstract:

    This paper deals with the problem of decision-level fusion for Vehicle Detection from gray-scale images. Specifically, the outputs of some classifiers are simply "distances", that is, they represent "distance measurements" between a query pattern and a decision boundary. We argue that the distance component is very helpful for decision fusion. Unfortunately, some of the most popular statistical decision fusion rules, such as the Sum rule and Product rule, do not take advantage of the "distance" property. Even worse, these rules make assumptions about data independence and distribution models which do not hold in practice. Motivated by these observations, we propose a simple decision-level fusion rule in the context of Vehicle Detection. Our fusion rule takes advantage of "distance" information and does not make any assumptions. We have applied this rule on a Vehicle Detection problem, showing that it outperforms well known statistical fusion rules.

  • On-road Vehicle Detection: A review
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
    Co-Authors: Zehang Sun, George Bebis, Ronald Miller
    Abstract:

    Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other Vehicles has attracted a lot of attention lately. In these systems, robust and reliable Vehicle Detection is a critical step. This paper presents a review of recent vision-based on-road Vehicle Detection systems. Our focus is on systems where the camera is mounted on the Vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road Vehicle Detection using optical sensors followed by a brief review of intelligent Vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based Vehicle Detection. Methods aiming to quickly hypothesize the location of Vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating Detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for Vehicle Detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

  • Monocular precrash Vehicle Detection: features and classifiers
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2006
    Co-Authors: Zehang Sun, George Bebis, Ronald Hugh Miller
    Abstract:

    Robust and reliable Vehicle Detection from images acquired by a moving Vehicle (i.e., on-road Vehicle Detection) is an important problem with applications to driver assistance systems and autonomous, self-guided Vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view Vehicle Detection. Specifically, by treating the problem of Vehicle Detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular Vehicle Detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average Detection rate of 10 Hz. Our Vehicle Detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where Vehicles might be present are extracted. This step uses multiscale techniques not only to speed up Detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept Vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

Hui Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Preceding Vehicle Detection Method Based on Visual Fusion
    DEStech Transactions on Computer Science and Engineering, 2019
    Co-Authors: Yi Liang, Hui Zhou
    Abstract:

    When the general preceding Vehicle Detection is carried for monocular vision, the accuracy of the Detection is affected by the dynamic background, illumination and occlusion. This paper proposed a preceding Vehicle Detection method fusing monocular and binocular vision method. Firstly, the potential area of the preceding Vehicle was established by using SSD (Single Shot Multibox Detection) algorithm, then, the binocular vision-based Vehicle Detection method for the potential area was used, its U-V disparity was calculated, and the accurate preceding Vehicle position by the Vehicle depth information in the image was obtained; finally, the missed Detection and false Detections in the monocular vision detector were removed with the U-V disparity Detections obtained by binocular vision detector. And the final results combined with the type and position information were outputted. Thereby the accuracy of the preceding Vehicle Detection was improved. The test result showed that on the condition of KITTI date set the visual fusion method can effectively improve the accuracy of Vehicle Detection. And compared with SSD algorithm in monocular, the Detection Rate was increased by 1.46%, the Recall Rate was increased by 2.83%, the Missed Rate was reduced by 2.83% and the False Rate was reduced by 1.10% than the monocular SSD algorithm.

Zehang Sun - One of the best experts on this subject based on the ideXlab platform.

  • Decision-level fusion for Vehicle Detection
    2007
    Co-Authors: Zehang Sun, George Bebis, Nikolaos G. Bourbakis
    Abstract:

    This paper deals with the problem of decision-level fusion for Vehicle Detection from gray-scale images. Specifically, the outputs of some classifiers are simply "distances", that is, they represent "distance measurements" between a query pattern and a decision boundary. We argue that the distance component is very helpful for decision fusion. Unfortunately, some of the most popular statistical decision fusion rules, such as the Sum rule and Product rule, do not take advantage of the "distance" property. Even worse, these rules make assumptions about data independence and distribution models which do not hold in practice. Motivated by these observations, we propose a simple decision-level fusion rule in the context of Vehicle Detection. Our fusion rule takes advantage of "distance" information and does not make any assumptions. We have applied this rule on a Vehicle Detection problem, showing that it outperforms well known statistical fusion rules.

  • On-road Vehicle Detection: A review
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
    Co-Authors: Zehang Sun, George Bebis, Ronald Miller
    Abstract:

    Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other Vehicles has attracted a lot of attention lately. In these systems, robust and reliable Vehicle Detection is a critical step. This paper presents a review of recent vision-based on-road Vehicle Detection systems. Our focus is on systems where the camera is mounted on the Vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road Vehicle Detection using optical sensors followed by a brief review of intelligent Vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based Vehicle Detection. Methods aiming to quickly hypothesize the location of Vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating Detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for Vehicle Detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

  • Monocular precrash Vehicle Detection: features and classifiers
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2006
    Co-Authors: Zehang Sun, George Bebis, Ronald Hugh Miller
    Abstract:

    Robust and reliable Vehicle Detection from images acquired by a moving Vehicle (i.e., on-road Vehicle Detection) is an important problem with applications to driver assistance systems and autonomous, self-guided Vehicles. The focus of this work is on the issues of feature extraction and classification for rear-view Vehicle Detection. Specifically, by treating the problem of Vehicle Detection as a two-class classification problem, we have investigated several different feature extraction methods such as principal component analysis, wavelets, and Gabor filters. To evaluate the extracted features, we have experimented with two popular classifiers, neural networks and support vector machines (SVMs). Based on our evaluation results, we have developed an on-board real-time monocular Vehicle Detection system that is capable of acquiring grey-scale images, using Ford's proprietary low-light camera, achieving an average Detection rate of 10 Hz. Our Vehicle Detection algorithm consists of two main steps: a multiscale driven hypothesis generation step and an appearance-based hypothesis verification step. During the hypothesis generation step, image locations where Vehicles might be present are extracted. This step uses multiscale techniques not only to speed up Detection, but also to improve system robustness. The appearance-based hypothesis verification step verifies the hypotheses using Gabor features and SVMs. The system has been tested in Ford's concept Vehicle under different traffic conditions (e.g., structured highway, complex urban streets, and varying weather conditions), illustrating good performance.

Tong Boon Tang - One of the best experts on this subject based on the ideXlab platform.

  • Vehicle Detection techniques for collision avoidance systems a review
    IEEE Transactions on Intelligent Transportation Systems, 2015
    Co-Authors: Amir Mukhtar, Likun Xia, Tong Boon Tang
    Abstract:

    Over the past decade, vision-based Vehicle Detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in Vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based Vehicle Detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a Vehicle Detection processes starting from sensor selection to Vehicle Detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle Detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in Vehicle industries.