A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

Romanoni, Andrea; Mussone, Lorenzo; Rizzi, Davide; Matteucci, Matteo

doi:10.1016/j.patrec.2014.09.003

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…In Table 6 we compared the proposed system, which jointly estimates the trajectory, the class and the dimensions of the tracked vehicles, against the system proposed in [28] which performs a disjoint estimation of the class after the trajectory estimation through the Backward-Simulation Particle Smoother (i.e., in [28] the Backward-Simulation Particle Smoother estimates the trajectory for a set of different models independently, then the proposed system chooses the most likely model). The results in the table show how the joint estimation improves significantly the accuracy of the Fig.…”

Section: Real Datasetsmentioning

confidence: 99%

“…To take into account the twofold nature of this state representation, we model the transition between classes as a Markov Chain. The second contribution is the estimation by means of a Backward-Simulation Parti- The proposed system significantly improves the capabilities of the systems presented in [17,26,28]. In those contributions we developed two Monte Carlo estimation systems to reconstruct the trajectory of a vehicle and to estimate its class independently: one of the two systems implements a Viterbibased approach, while the other is a Particle Smoother.…”

Section: Introductionmentioning

confidence: 99%

“…In those contributions we developed two Monte Carlo estimation systems to reconstruct the trajectory of a vehicle and to estimate its class independently: one of the two systems implements a Viterbibased approach, while the other is a Particle Smoother. In [28], we showed the reasons why the Particle approach overcomes the Viterbi-based algorithm. The current paper presents a significantly different approach; indeed, we propose a single Monte Carlo estimator to obtain a joint estimation of the trajectory, the class and the dimensions of tracked vehicles; conversely, in [17,26], we employed a set of independent estimators, one for each fixed-sized model, then we choose the most likely one.…”

Section: Introductionmentioning

confidence: 99%

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Backward-Simulation Particle Smoother with a hybrid state for 3D vehicle trajectory, class and dimension simultaneous estimation

Romanoni

Sorrenti

Matteucci

2015

Machine Vision and Applications

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The estimation of the 3D trajectory, the class and the dimensions of a vehicle represents three relevant tasks for traffic monitoring. They are usually performed by separate sub-systems and only few existing algorithms cope with the three tasks at the same time. However, if these tasks are integrated, the trajectory estimation enforces the classification with temporal consistency, and at the same time, the estimation of the vehicle class and dimensions can be used to increase the trajectory estimate accuracy. In this work, we propose an algorithm to estimate the 3D trajectory, the class and the dimensions of vehicles simultaneously by means of a Backward-Simulation Particle Smoother whose state contains both continuous (vehicle pose and dimensions), and discrete (vehicle class) quantities. To integrate the class estimate in the Particle Smoother we model the class prediction as a Markov Chain. We performed experimental tests on both simulated and real datasets; they show that the pose and dimension estimation reaches centimeter-accuracy and the classification accuracy is higher than 95 %.

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Section: Real Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Backward-Simulation Particle Smoother with a hybrid state for 3D vehicle trajectory, class and dimension simultaneous estimation

Romanoni

Sorrenti

Matteucci

2015

Machine Vision and Applications

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“…With the development of social economy and the extensive use of Location-Based Services (LBS), Global Positioning System (GPS) technology in the application of vehicle positioning and monitoring is entering the mature stage [1]. Vehicle trajectory display is one of the important functions of map navigation, positioning, and traffic management decision [2], [3]. The principle is to periodically obtain vehicle positioning information using GPS and send the information to a remote server through wireless network.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Trajectory Optimization Based on Limiting Average Algorithm

Wang

Liu

2021

IEEE Access

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“…In the past two decades, image sensors associated with the everincreasing power of modern computing systems are increasingly affordable, and hence there has been widespread deployment of camera-based vision systems for traffic monitoring, traffic management, traffic data collection, traffic accident warning, etc. [3][4][5][6]. Among many versatile applications of these camera-based vision systems, one of them is automated traffic data collection, which can significantly improve efficiency and reduce cost compared to manual data collection.…”

Section: Introductionmentioning

confidence: 99%

Development of a tracking-based system for automated traffic data collection for roundabouts

Dinh

Tang

2017

J. Mod. Transport.

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Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kernel-based tracking and overlap-based optimization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.

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A comparison of two Monte Carlo algorithms for 3D vehicle trajectory reconstruction in roundabouts

Cited by 9 publications

References 27 publications

Backward-Simulation Particle Smoother with a hybrid state for 3D vehicle trajectory, class and dimension simultaneous estimation

Backward-Simulation Particle Smoother with a hybrid state for 3D vehicle trajectory, class and dimension simultaneous estimation

Vehicle Trajectory Optimization Based on Limiting Average Algorithm

Development of a tracking-based system for automated traffic data collection for roundabouts

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