On the other hand, the performance of a model in action recognition is heavily affected by domain shift.
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor.
In a preliminary work, we presented a simple, yet powerful, method to copy black-box models by querying them with natural random images.
By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for autonomous vehicles.
Ranked #6 on Lane Detection on LLAMAS
In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps.
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
Ranked #9 on Lane Detection on LLAMAS
The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents.
We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car.
Deep learning has been successfully applied to several problems related to autonomous driving.
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
However, none of them combine the power of the deep learning-based detectors with prior maps to recognize the state of the relevant traffic lights.
To implement this idea we derive specialized deep models for each domain by adapting a pre-trained architecture but, differently from other methods, we propose a novel strategy to automatically adjust the computational complexity of the network.
no code implementations • 14 Jan 2019 • Claudine Badue, Rânik Guidolini, Raphael Vivacqua Carneiro, Pedro Azevedo, Vinicius Brito Cardoso, Avelino Forechi, Luan Ferreira Reis Jesus, Rodrigo Ferreira Berriel, Thiago Meireles Paixão, Filipe Mutz, Thiago Oliveira-Santos, Alberto Ferreira De Souza
In this survey, we present the typical architecture of the autonomy system of self-driving cars.
The objective of map decay is to correct invalid occupancy probabilities of map cells that are unobservable by sensors.
The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i. e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes).
The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network.
Many crosswalk classification, detection and localization systems have been proposed in the literature over the years.
One of them is known as place recognition, which associates images of places with their corresponding positions.
We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations.
In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks.