Deep Learning Neural Networks for Automatic Vehicle Incident Detection

A tool for Emergency Detection with Deep Learning Neural Networks

The ubiquitous presence of sensor networks, control units and detection devices allows for a significant availability of data. The increased computational power also encourages a wider development of deep neural networks that represent data in multiple levels of abstraction. In this contribution we present a tool that process the daily precipitation amount in Tuscany region and the emergency si...

Freeway Incident Detection Using Artificial Neural Networks

Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep lea...

Multispectral Deep Neural Networks for Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock applications, e.g., surveillance and autonomous driving. We deeply analyze Faster R-CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet-based pedestrian detectors trained by color or thermal images separately provide compleme...

Deep Convolutional Neural Networks for pedestrian detection

Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular convolutional neural network...