292 research outputs found
Markov random fields for abnormal behavior detection on highways
This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results show that abnormal behavior for a pedestrian walking, running and cycling on the highway, is detected with 82% accuracy at the 10% false alarm rate, and the system has an overall accuracy of 86% on the test data
License to Supervise:Influence of Driving Automation on Driver Licensing
To use highly automated vehicles while a driver remains responsible for safe driving, places new â yet demanding, requirements on the human operator. This is because the automation creates a gap between driversâ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects driversâ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation
Predicting pedestrian crossing intentions using contextual information
El entorno urbano es uno de los escenarios m as complejos para un veh culo aut onomo, ya
que lo comparte con otros tipos de usuarios conocidos como usuarios vulnerables de la
carretera, con los peatones como mayor representante. Estos usuarios se caracterizan por
su gran dinamicidad. A pesar del gran n umero de interacciones entre veh culos y peatones,
la seguridad de estos ultimos no ha aumentado al mismo ritmo que la de los ocupantes de
los veh culos. Por esta raz on, es necesario abordar este problema. Una posible estrategia
estar a basada en conseguir que los veh culos anticipen el comportamiento de los peatones
para minimizar situaciones de riesgo, especialmente presentes en el momento de cruce.
El objetivo de esta tesis doctoral es alcanzar dicha anticipaci on mediante el desarrollo
de t ecnicas de predicci on de la acci on de cruce de peatones basadas en aprendizaje
profundo.
Previo al dise~no e implementaci on de los sistemas de predicci on, se ha desarrollado
un sistema de clasi caci on con el objetivo de discernir a los peatones involucrados en la
escena vial. El sistema, basado en redes neuronales convolucionales, ha sido entrenado y
validado con un conjunto de datos personalizado. Dicho conjunto se ha construido a partir
de varios conjuntos existentes y aumentado mediante la inclusi on de im agenes obtenidas de
internet. Este paso previo a la anticipaci on permitir a reducir el procesamiento innecesario
dentro del sistema de percepci on del veh culo.
Tras este paso, se han desarrollado dos sistemas como propuesta para abordar el problema
de predicci on.
El primer sistema, basado en redes convolucionales y recurrentes, obtiene una predicci
on a corto plazo de la acci on de cruce realizada un segundo en el futuro. La informaci on
de entrada al modelo est a basada principalmente en imagen, que permite aportar contexto
adicional del peat on. Adem as, el uso de otras variables relacionadas con el peat on junto
con mejoras en la arquitectura, permiten mejorar considerablemente los resultados en el
conjunto de datos JAAD.
El segundo sistema se basa en una arquitectura end-to-end basado en la combinaci on
de redes neuronales convolucionales tridimensionales y/o el codi cador de la arquitectura
Transformer. En este modelo, a diferencia del anterior, la mayor a de las mejoras est an
centradas en transformaciones de los datos de entrada. Tras analizar dichas mejoras,
una serie de modelos se han evaluado y comparado con otros m etodos utilizando tanto el
conjunto de datos JAAD como PIE. Los resultados obtenidos han conseguido liderar el
estado del arte, validando la arquitectura propuesta.The urban environment is one of the most complex scenarios for an autonomous vehicle,
as it is shared with other types of users known as vulnerable road users, with pedestrians
as their principal representative. These users are characterized by their great dynamicity.
Despite a large number of interactions between vehicles and pedestrians, the safety of
pedestrians has not increased at the same rate as that of vehicle occupants. For this
reason, it is necessary to address this problem. One possible strategy would be anticipating
pedestrian behavior to minimize risky situations, especially during the crossing.
The objective of this doctoral thesis is to achieve such anticipation through the development
of crosswalk action prediction techniques based on deep learning.
Before the design and implementation of the prediction systems, a classi cation system
has been developed to discern the pedestrians involved in the road scene. The system,
based on convolutional neural networks, has been trained and validated with a customized
dataset. This set has been built from several existing sets and augmented by including
images obtained from the Internet. This pre-anticipation step would reduce unnecessary
processing within the vehicle perception system.
After this step, two systems have been developed as a proposal to solve the prediction
problem.
The rst system is composed of convolutional and recurrent encoder networks. It
obtains a short-term prediction of the crossing action performed one second in the future.
The input information to the model is mainly image-based, which provides additional
pedestrian context. In addition, the use of pedestrian-related variables and architectural
improvements allows better results on the JAAD dataset.
The second system is an end-to-end architecture based on the combination of threedimensional
convolutional neural networks and/or the Transformer architecture encoder.
In this model, most of the proposed and investigated improvements are focused on transformations
of the input data. After an extensive set of individual tests, several models
have been trained, evaluated, and compared with other methods using both JAAD and
PIE datasets. Obtained results are among the best state-of-the-art models, validating the
proposed architecture
Policing, Technology, and Doctrinal Assists
Sounding the alarm about technology, policing, and privacy has become an almost daily occurrence. We are told that the governmentâs use of technology as a surveillance tool is an âinsidious assault on our freedom.â That it is ânearly impossible to live today without generating thousands of records about what we watch, read, buy and doâand the government has access to them.â The message is clear. Big Brother is watching. And we should be afraid.
But the police use of technology, or what this Article terms âtechno-policing,â does not have to be dystopian. This Article challenges conventional thinking and offers an entirely new way to think about technology and policing. Deployed properly, techno-policingâfrom the use of simple smartphone applications such as FaceTime and Google Hangout, to the deployment of high-tech surveillance cameras, terahertz scanners, Big Data, and Automated Suspicion Algorithmsâcan enhance the warrant requirement and the goals of transparency and accuracy. And at this time when crime levels are relatively low and there are growing demands for police accountabilityâthink Black Lives Matterâtechno-policing can enhance legitimacy. Most importantly, techno-policing can provide much needed doctrinal assists where Fourth Amendment doctrine alone has proved inadequate, shortsighted, and unfair
A mathematical model for computerized car crash detection using computer vision techniques
My proposed approach to the automatic detection of traffic accidents in a signalized intersection is presented here. In this method, a digital camera is strategically placed to view the entire intersection. The images are captured, processed and analyzed for the presence of vehicles and pedestrians in the proposed detection zones. Those images are further processed to detect if an accident has occurred; The mathematical model presented is a Poisson distribution that predicts the number of accidents in an intersection per week, which can be used as approximations for modeling the crash process. We believe that the crash process can be modeled by using a two-state method, which implies that the intersection is in one of two states: clear (no accident) or obstructed (accident). We can then incorporate a rule-based AI system, which will help us in identifying that a crash has taken or will possibly take place; We have modeled the intersection as a service facility, which processes vehicles in a relatively small amount of time. A traffic accident is then perceived as an interruption of that service
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