Vehicle Tracking Using Projective Particle Filter

Bouttefroy, P. L. M.; Bouzerdoum, A.; Phung, Son Lam; Beghdadi, Azeddine

doi:10.1109/avss.2009.60

Cited by 22 publications

(7 citation statements)

References 17 publications

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“…Automated Crash Notification (ACN) systems exploit the telemetric data from the collided vehicles to inform the emergency services in order to reduce fatalities from car accidents [ 9 ]. Visual sensors (surveillance cameras) are also widely used to monitor driver and vehicle behavior by tracking vehicle trajectories near traffic lights or on highways to monitor traffic flow and road disruptions [ 10 , 11 ] A robust visual monitoring system would detect the abnormal events and instantly inform the relevant authority [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Almaadeed

Asim

Al‐Maadeed

et al. 2018

Sensors

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This work investigates the problem of detecting hazardous events on roads by designing an audio surveillance system that automatically detects perilous situations such as car crashes and tire skidding. In recent years, research has shown several visual surveillance systems that have been proposed for road monitoring to detect accidents with an aim to improve safety procedures in emergency cases. However, the visual information alone cannot detect certain events such as car crashes and tire skidding, especially under adverse and visually cluttered weather conditions such as snowfall, rain, and fog. Consequently, the incorporation of microphones and audio event detectors based on audio processing can significantly enhance the detection accuracy of such surveillance systems. This paper proposes to combine time-domain, frequency-domain, and joint time-frequency features extracted from a class of quadratic time-frequency distributions (QTFDs) to detect events on roads through audio analysis and processing. Experiments were carried out using a publicly available dataset. The experimental results conform the effectiveness of the proposed approach for detecting hazardous events on roads as demonstrated by 7% improvement of accuracy rate when compared against methods that use individual temporal and spectral features.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Almaadeed

Asim

Al‐Maadeed

et al. 2018

Sensors

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“…Among them, Kalman filter (KF) is proved to be the optimal estimator for linear systems with Gaussian-distributed states [3] [4] and Extended Kalman filter (EKF) is utilized for non-linear systems [5]. However, since it is hard to accurately represent the state distribution by simple multivariate Gaussian distributions in the real world, Particle filter (PF) has advantages over variants of Kalman filter since it has no limitations on the form of the system and state distributions [6]- [9]. In [10] and [11], grid-based particle filters were used to estimate the dynamics of the traffic environment.…”

Section: Introductionmentioning

confidence: 99%

Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter

Zhan

Tomizuka

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Accurate and robust tracking of surrounding road participants plays an important role in autonomous driving. However, there is usually no prior knowledge of the number of tracking targets due to object emergence, object disappearance and false alarms. To overcome this challenge, we propose a generic vehicle tracking framework based on modified mixture particle filter, which can make the number of tracking targets adaptive to real-time observations and track all the vehicles within sensor range simultaneously in a uniform architecture without explicit data association. Each object corresponds to a mixture component whose distribution is non-parametric and approximated by particle hypotheses. Most tracking approaches employ vehicle kinematic models as the prediction model. However, it is hard for these models to make proper predictions when sensor measurements are lost or become low quality due to partial or complete occlusions. Moreover, these models are incapable of forecasting sudden maneuvers. To address these problems, we propose to incorporate learning-based behavioral models instead of pure vehicle kinematic models to realize prediction in the prior update of recursive Bayesian state estimation. Two typical driving scenarios including lane keeping and lane change are demonstrated to verify the effectiveness and accuracy of the proposed framework as well as the advantages of employing learning-based models.

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“…Other approaches for the selection of the importance density have also been proposed in the field of vehicle tracking. For instance, in (Bouttefroy, 2009), the linear fractional transformation is integrated into the importance density. This transformation allows to estimate the position of the object on the camera plane by projecting its position in the road.…”

Section: Contour-based Trackingmentioning

confidence: 99%

“…• Dynamic model: vehicles are assumed to move smoothly in highways, therefore constant-velocity (Meier and Ade, 1998;Xiong and Debrunner, 2004) or random walk models (Han et al, 2005;Bouttefroy, 2009), in which the movement of the particles is given by a Gaussian distribution, are frequently used. The bicycle model is also used in some works (e.g.…”

Section: Contour-based Trackingmentioning

confidence: 99%

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Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

Laborda¹

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Aunque los que me conocen ya saben que soy más bien parco en palabras, no quiero dejar pasar esta oportunidad para reconocer en estas líneas a algunas personas cuya ayuda ha sido imprescindible para la realización de esta tesis. Para empezar, cómo no, quiero agradecer a mi director de tesis, Luis Salgado, por muchos motivos. Por supuesto, por ser el responsable de proponer, impulsar y conducir la investigación en el tema que aborda la tesis. Quiero agradecerte toda la sabiduría, paciencia, esfuerzo e ilusión que has puesto en esta tesis, has hecho una labor modélica. Creo honestamente que hemos hecho un buen equipo, no sólo a nivel científico, sino también humano. Sé que no te gusta que te lo digan, pero a lo largo todos estos años he aprendido mucho de ti o, si lo prefieres, contigo, a mucho niveles. Sabes que no me suelo prodigar en este tipo de digresiones: me cuesta tanto formularlas como a ti encajarlas. En cualquier caso, es de justicia, porque la mitad de esta tesis es tuya, y lo sabes.Quiero agradecer también a todos los profesores del GTI, en especial a Narciso por liderar el grupo y por la amabilidad y buena disposición que siempre ha mostrado conmigo, y por supuesto también a Fernando, Francisco, Julián y Nacho. Me gustaría agradecer a todos mis compañeros y ex-compañeros de fatigas en las lides del doctorado: a Raúl, eres un tío de 10; a Marcos, siempre recordaré nuestras luchas conjuntas contra OpenCV y nuestras conversaciones bajo los arcos de Turín; a César, por estar siempre ahí; a Carlos del Blanco, Carlos Cuevas y Guillermo por la paciencia que han demostrado al aguantarme durante estos últimos meses de estrés; y a todos los demás, por crear un ambiente de trabajo inigualable. No me olvido de todos los estudiantes de intercambio, especialmente de Ivana, Philippe y Hui, con los que he pasado grandes momentos que recuerdo con mucho cariño.Me acuerdo también de toda la gente, los amigos, que he conocido durante estos años en Madrid: Rafa,

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Vehicle Tracking Using Projective Particle Filter

Cited by 22 publications

References 17 publications

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Automatic Detection and Classification of Audio Events for Road Surveillance Applications

Generic Vehicle Tracking Framework Capable of Handling Occlusions Based on Modified Mixture Particle Filter

Vision-based vehicle detection and tracking with a mobile camera using a statistical framework

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