Detection of moving objects in roundabouts based on a monocular system

Hassannejad, Hamid; Medici, Paolo; Cardarelli, Elena; Cerri, Pietro

doi:10.1016/j.eswa.2015.01.032

Cited by 31 publications

(6 citation statements)

References 53 publications

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“…The system uses both a classification algorithm (based on Haar feature and AdaBoost) and a feature tracking algorithm that highlights moving obstacles. A similar algorithm is also developed for vehicle detection while merging a junction [26], but during the final test it was not enabled because it was not fully tested.…”

Section: A Perceptionmentioning

confidence: 99%

PROUD—Public Road Urban Driverless-Car Test

Broggi

Cerri

Debattisti

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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This paper presents an autonomous driving test held in Parma on urban roads and freeways open to regular traffic. During this test, the vehicle not only performed simple maneuvers, but it had to cope with complex driving scenarios as well, including roundabouts, junctions, pedestrian crossings, freeway junctions, and traffic lights. The test demonstrated the ability of the current technology to manage real situations and not only the well-structured and predictable ones. A comparison of milestones, challenges, and key results in autonomous driving is presented to highlight the novelty and the specific purpose of the test. The whole system is described: the vehicle; the software architecture; details about high-, medium-, and low-level control; and details about perception algorithms. A conclusion highlights the achieved results and draws possible directions for future development.Alberto Broggi received the Dr.Ing. (master's) degree in electronic engineering and the Ph.D. degree in information technology both from the Università degli Studi di Parma, Parma, Italy, in 1990 and 1994, respectively. He is currently a Full Professor with the Università degli Studi di Parma, where he is also the President and CEO of the VisLab spinoff company. He is an author of more than 150 publications on international scientific journals, book chapters, and refereed conference proceedings. Dr. Broggi served as Editor-in-Chief of IEEE TRANSACTIONS ON IN-TELLIGENT TRANSPORTATION SYSTEMS for the term 2004-

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Section: A Perceptionmentioning

confidence: 99%

PROUD—Public Road Urban Driverless-Car Test

Broggi

Cerri

Debattisti

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…If the pose of the object changes, then the classical Lucas-Kanade affine tracker can be employed ( Lucas & Kanade, 1981 ). In order to obtain a tracking system that is robust to the change of shapes and viewing positions of the vehicles, the corners or points of interest of the deformable vehicular objects are determined and features such as the histograms of oriented gradients (HOGs) ( Niknejad, Takeuchi, Mita, & McAllester, 2012;Olmedo, Sastre, Bascon, & Caballero, 2013 ), the speeded up robust features (SURFs), the scale invariant feature transforms (SIFTs) ( Lu, Izumi, Teng, & Wang, 2014;Mantripragada, Trigo, Martins, & Fleury, 2013;Shi & Tomasi, 1994 ) and the binary robust invariant scalable keypoint (BRISK) features ( Hassannejad, Medici, Cardarelli, & Cerri, 2015 ) are obtained from these points. Due to the fact that the vehicles are identified and their positions are estimated using the HOG, SURF, SIFT or BRISK features generated from the points of interest only, the tracking trajectories estimated from these methods may not provide satisfactory performance for occlusions or noisy environments.…”

Section: Introductionmentioning

confidence: 99%

Video-based tracking of vehicles using multiple time-spatial images

Mithun

Howlader

Rahman

2016

Expert Systems with Applications

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“…HV approaches for hypothesis confirmation include boosting algorithms such as AdaBoost [17,18], Support Vector Machines [19,20], or deep learning systems such as Convolutional Neural Networks (CNNs) [21], very deep CNNs [22] or region-based CNNs (R-CNN) such as Faster R-CNN [23,24] or Mask R-CNN [25]. Apart from typical two-step detectors, other techniques use e.g., pavement or lane detection for ROI alignment followed by illumination variation or edge filters to verify potential object candidates [26,27], a two-stage regression procedure [28] or the combination of appearance-based HG and motion-based HV with Haar-like features and AdaBoost [29].…”

Section: Introductionmentioning

confidence: 99%

The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study

Weber,

Kanarachos

2019

Sensors

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Automated vehicles will provide greater transport convenience and interconnectivity, increase mobility options to young and elderly people, and reduce traffic congestion and emissions. However, the largest obstacle towards the deployment of automated vehicles on public roads is their safety evaluation and validation. Undeniably, the role of cameras and Artificial Intelligence-based (AI) vision is vital in the perception of the driving environment and road safety. Although a significant number of studies on the detection and tracking of vehicles have been conducted, none of them focused on the role of vertical vehicle dynamics. For the first time, this paper analyzes and discusses the influence of road anomalies and vehicle suspension on the performance of detecting and tracking driving objects. To this end, we conducted an extensive road field study and validated a computational tool for performing the assessment using simulations. A parametric study revealed the cases where AI-based vision underperforms and may significantly degrade the safety performance of AVs.

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Detection of moving objects in roundabouts based on a monocular system

Cited by 31 publications

References 53 publications

PROUD—Public Road Urban Driverless-Car Test

PROUD—Public Road Urban Driverless-Car Test

Video-based tracking of vehicles using multiple time-spatial images

The Correlation between Vehicle Vertical Dynamics and Deep Learning-Based Visual Target State Estimation: A Sensitivity Study

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