1,464 research outputs found
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
A system for learning statistical motion patterns
Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Perception technologies in Autonomous Driving are experiencing their golden
age due to the advances in Deep Learning. Yet, most of these systems rely on
the semantically rich information of RGB images. Deep Learning solutions
applied to the data of other sensors typically mounted on autonomous cars (e.g.
lidars or radars) are not explored much. In this paper we propose a novel
solution to understand the dynamics of moving vehicles of the scene from only
lidar information. The main challenge of this problem stems from the fact that
we need to disambiguate the proprio-motion of the 'observer' vehicle from that
of the external 'observed' vehicles. For this purpose, we devise a CNN
architecture which at testing time is fed with pairs of consecutive lidar
scans. However, in order to properly learn the parameters of this network,
during training we introduce a series of so-called pretext tasks which also
leverage on image data. These tasks include semantic information about
vehicleness and a novel lidar-flow feature which combines standard image-based
optical flow with lidar scans. We obtain very promising results and show that
including distilled image information only during training, allows improving
the inference results of the network at test time, even when image data is no
longer used.Comment: Presented in IEEE ICRA 2018. IEEE Copyrights: Personal use of this
material is permitted. Permission from IEEE must be obtained for all other
uses. (V2 just corrected comments on arxiv submission
Wrong Way Vehicle Detection in Single and Double Lane
Wrong-way driving is one of the primary causes of traffic jams and accidents globally. It is possible to identify vehicles going the wrong direction, which lessens accidents and traffic congestion. Surveillance footage has become an important source of data due to the accessibility of less priced cameras and the expanding use of real-time traffic management systems. In this paper, we propose a technique for automatically identifying automobiles moving against traffic. Our system uses the You Only Look Once (CNN) algorithm to recognize and track vehicles from video inputs and the centroid tracking method to determine each vehicle's orientation inside a given region of interest (ROI) in order to identify vehicles traveling in the wrong direction. It functions in three steps. The Deep sort tracking method is particularly good in detecting and tracking objects, and the centroid tracking technique can effectively monitor the direction of travel. Experiments with a variety of traffic films show that the suggested method can detect and identify wrong-way moving vehicles in a variety of lighting and weather scenarios. The interface of the system is quite simple and easy to use
Lidar-based scene understanding for autonomous driving using deep learning
With over 1.35 million fatalities related to traffic accidents worldwide, autonomous driving was foreseen at the beginning of this century as a feasible solution to improve security in our roads. Nevertheless, it is meant to disrupt our transportation paradigm, allowing to reduce congestion, pollution, and costs, while increasing the accessibility, efficiency, and reliability of the transportation for both people and goods. Although some advances have gradually been transferred into commercial vehicles in the way of Advanced Driving Assistance Systems (ADAS) such as adaptive cruise control, blind spot detection or automatic parking, however, the technology is far from mature. A full understanding of the scene is actually needed so that allowing the vehicles to be aware of the surroundings, knowing the existing elements of the scene, as well as their motion, intentions and interactions.
In this PhD dissertation, we explore new approaches for understanding driving scenes from 3D LiDAR point clouds by using Deep Learning methods. To this end, in Part I we analyze the scene from a static perspective using independent frames to detect the neighboring vehicles. Next, in Part II we develop new ways for understanding the dynamics of the scene. Finally, in Part III we apply all the developed methods to accomplish higher level challenges such as segmenting moving obstacles while obtaining their rigid motion vector over the ground.
More specifically, in Chapter 2 we develop a 3D vehicle detection pipeline based on a multi-branch deep-learning architecture and propose a Front (FR-V) and a Bird’s Eye view (BE-V) as 2D representations of the 3D point cloud to serve as input for training our models. Later on, in Chapter 3 we apply and further test this method on two real uses-cases, for pre-filtering moving
obstacles while creating maps to better localize ourselves on subsequent days, as well as for vehicle tracking. From the dynamic perspective, in Chapter 4 we learn from the 3D point cloud a novel dynamic feature that resembles optical flow from RGB images. For that, we develop a new approach to leverage RGB optical flow as pseudo ground truth for training purposes but allowing the use of only 3D LiDAR data at inference time. Additionally, in Chapter 5 we explore the benefits of combining classification and regression learning problems to face the optical flow estimation task in a joint coarse-and-fine manner. Lastly, in Chapter 6 we gather the previous methods and demonstrate that with these independent tasks we can guide the learning of higher challenging problems such as segmentation and motion estimation of moving vehicles from our own moving perspective.Con más de 1,35 millones de muertes por accidentes de tráfico en el mundo, a principios de siglo se predijo que la conducciĂłn autĂłnoma serĂa una soluciĂłn viable para mejorar la seguridad en nuestras carreteras. Además la conducciĂłn autĂłnoma está destinada a cambiar nuestros paradigmas de transporte, permitiendo reducir la congestiĂłn del tráfico, la contaminaciĂłn y el coste, a la vez que aumentando la accesibilidad, la eficiencia y confiabilidad del transporte tanto de personas como de mercancĂas. Aunque algunos avances, como el control de crucero adaptativo, la detecciĂłn de puntos ciegos o el estacionamiento automático, se han transferido gradualmente a vehĂculos comerciales en la forma de los Sistemas Avanzados de Asistencia a la ConducciĂłn (ADAS), la tecnologĂa aĂşn no ha alcanzado el suficiente grado de madurez. Se necesita una comprensiĂłn completa de la escena para que los vehĂculos puedan entender el entorno, detectando los elementos presentes, asĂ como su movimiento, intenciones e interacciones. En la presente tesis doctoral, exploramos nuevos enfoques para comprender escenarios de conducciĂłn utilizando nubes de puntos en 3D capturadas con sensores LiDAR, para lo cual empleamos mĂ©todos de aprendizaje profundo. Con este fin, en la Parte I analizamos la escena desde una perspectiva estática para detectar vehĂculos. A continuaciĂłn, en la Parte II, desarrollamos nuevas formas de entender las dinámicas del entorno. Finalmente, en la Parte III aplicamos los mĂ©todos previamente desarrollados para lograr desafĂos de nivel superior, como segmentar obstáculos dinámicos a la vez que estimamos su vector de movimiento sobre el suelo. EspecĂficamente, en el CapĂtulo 2 detectamos vehĂculos en 3D creando una arquitectura de aprendizaje profundo de dos ramas y proponemos una vista frontal (FR-V) y una vista de pájaro (BE-V) como representaciones 2D de la nube de puntos 3D que sirven como entrada para entrenar nuestros modelos. Más adelante, en el CapĂtulo 3 aplicamos y probamos aĂşn más este mĂ©todo en dos casos de uso reales, tanto para filtrar obstáculos en movimiento previamente a la creaciĂłn de mapas sobre los que poder localizarnos mejor en los dĂas posteriores, como para el seguimiento de vehĂculos. Desde la perspectiva dinámica, en el CapĂtulo 4 aprendemos de la nube de puntos en 3D una caracterĂstica dinámica novedosa que se asemeja al flujo Ăłptico sobre imágenes RGB. Para ello, desarrollamos un nuevo enfoque que aprovecha el flujo Ăłptico RGB como pseudo muestras reales para entrenamiento, usando solo information 3D durante la inferencia. Además, en el CapĂtulo 5 exploramos los beneficios de combinar los aprendizajes de problemas de clasificaciĂłn y regresiĂłn para la tarea de estimaciĂłn de flujo Ăłptico de manera conjunta. Por Ăşltimo, en el CapĂtulo 6 reunimos los mĂ©todos anteriores y demostramos que con estas tareas independientes podemos guiar el aprendizaje de problemas de más alto nivel, como la segmentaciĂłn y estimaciĂłn del movimiento de vehĂculos desde nuestra propia perspectivaAmb mĂ©s d’1,35 milions de morts per accidents de trĂ nsit al mĂłn, a principis de segle es va
predir que la conducció autònoma es convertiria en una solució viable per millorar la seguretat
a les nostres carreteres. D’altra banda, la conducció autònoma està destinada a canviar els
paradigmes del transport, fent possible aixĂ reduir la densitat del trĂ nsit, la contaminaciĂł i
el cost, alhora que augmentant l’accessibilitat, l’eficiència i la confiança del transport tant de
persones com de mercaderies. Encara que alguns avenços, com el control de creuer adaptatiu,
la detecció de punts cecs o l’estacionament automà tic, s’han transferit gradualment a vehicles
comercials en forma de Sistemes Avançats d’Assistència a la Conducció (ADAS), la tecnologia
encara no ha arribat a aconseguir el grau suficient de maduresa. És necessà ria, doncs, una
total comprensió de l’escena de manera que els vehicles puguin entendre l’entorn, detectant els
elements presents, aixĂ com el seu moviment, intencions i interaccions.
A la present tesi doctoral, explorem nous enfocaments per tal de comprendre les diferents
escenes de conducció utilitzant núvols de punts en 3D capturats amb sensors LiDAR, mitjançant
l’ús de mètodes d’aprenentatge profund. Amb aquest objectiu, a la Part I analitzem l’escena des
d’una perspectiva està tica per a detectar vehicles. A continuació, a la Part II, desenvolupem
noves formes d’entendre les dinà miques de l’entorn. Finalment, a la Part III apliquem els
mètodes prèviament desenvolupats per a aconseguir desafiaments d’un nivell superior, com, per
exemple, segmentar obstacles dinĂ mics al mateix temps que estimem el seu vector de moviment
respecte al terra.
Concretament, al CapĂtol 2 detectem vehicles en 3D creant una arquitectura d’aprenentatge
profund amb dues branques, i proposem una vista frontal (FR-V) i una vista d’ocell (BE-V)
com a representacions 2D del nĂşvol de punts 3D que serveixen com a punt de partida per
entrenar els nostres models. MĂ©s endavant, al CapĂtol 3 apliquem i provem de nou aquest
mètode en dos casos d’ús reals, tant per filtrar obstacles en moviment prèviament a la creació
de mapes en els quals poder localitzar-nos millor en dies posteriors, com per dur a terme
el seguiment de vehicles. Des de la perspectiva dinĂ mica, al CapĂtol 4 aprenem una nova
caracterĂstica dinĂ mica del nĂşvol de punts en 3D que s’assembla al flux òptic sobre imatges
RGB. Per a fer-ho, desenvolupem un nou enfocament que aprofita el flux òptic RGB com pseudo
mostres reals per a entrenament, utilitzant només informació 3D durant la inferència. Després,
al CapĂtol 5 explorem els beneficis que s’obtenen de combinar els aprenentatges de problemes
de classificació i regressió per la tasca d’estimació de flux òptic de manera conjunta. Finalment,
al CapĂtol 6 posem en comĂş els mètodes anteriors i demostrem que mitjançant aquests processos
independents podem abordar l’aprenentatge de problemes més complexos, com la segmentació
i estimació del moviment de vehicles des de la nostra pròpia perspectiva
Deep lidar CNN to understand the dynamics of moving vehicles
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Perception technologies in Autonomous Driving are experiencing their golden age due to the advances in Deep Learning. Yet, most of these systems rely on the semantically rich information of RGB images. Deep Learning solutions applied to the data of other sensors typically mounted on autonomous cars (e.g. lidars or radars) are not explored much. In this paper we propose a novel solution to understand the dynamics of moving vehicles of the scene from only lidar information. The main challenge of this problem stems from the fact that we need to disambiguate the proprio-motion of the “observer” vehicle from that of the external “observed” vehicles. For this purpose, we devise a CNN architecture which at testing time is fed with pairs of consecutive lidar scans. However, in order to properly learn the parameters of this network, during training we introduce a series of so-called pretext tasks which also leverage on image data. These tasks include semantic information about vehicleness and a novel lidar-flow feature which combines standard image-based optical flow with lidar scans. We obtain very promising results and show that including distilled image information only during training, allows improving the inference results of the network at test time, even when image data is no longer used.Peer ReviewedPostprint (author's final draft
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