7 research outputs found
Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras
Event-based cameras, also known as neuromorphic cameras, are bioinspired
sensors able to perceive changes in the scene at high frequency with low power
consumption. Becoming available only very recently, a limited amount of work
addresses object detection on these devices. In this paper we propose two
neural networks architectures for object detection: YOLE, which integrates the
events into surfaces and uses a frame-based model to process them, and fcYOLE,
an asynchronous event-based fully convolutional network which uses a novel and
general formalization of the convolutional and max pooling layers to exploit
the sparsity of camera events. We evaluate the algorithm with different
extensions of publicly available datasets and on a novel synthetic dataset.Comment: accepted at CVPR2019 Event-based Vision Worksho
Attention Mechanisms for Object Recognition with Event-Based Cameras
Event-based cameras are neuromorphic sensors capable of efficiently encoding
visual information in the form of sparse sequences of events. Being
biologically inspired, they are commonly used to exploit some of the
computational and power consumption benefits of biological vision. In this
paper we focus on a specific feature of vision: visual attention. We propose
two attentive models for event based vision: an algorithm that tracks events
activity within the field of view to locate regions of interest and a
fully-differentiable attention procedure based on DRAW neural model. We
highlight the strengths and weaknesses of the proposed methods on four
datasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101
collections, using the Phased LSTM recognition network as a baseline reference
model obtaining improvements in terms of both translation and scale invariance.Comment: WACV2019 camera-ready submissio
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning
Event cameras sense intensity changes and have many advantages over
conventional cameras. To take advantage of event cameras, some methods have
been proposed to reconstruct intensity images from event streams. However, the
outputs are still in low resolution (LR), noisy, and unrealistic. The
low-quality outputs stem broader applications of event cameras, where high
spatial resolution (HR) is needed as well as high temporal resolution, dynamic
range, and no motion blur. We consider the problem of reconstructing and
super-resolving intensity images from LR events, when no ground truth (GT) HR
images and down-sampling kernels are available. To tackle the challenges, we
propose a novel end-to-end pipeline that reconstructs LR images from event
streams, enhances the image qualities and upsamples the enhanced images, called
EventSR. For the absence of real GT images, our method is primarily
unsupervised, deploying adversarial learning. To train EventSR, we create an
open dataset including both real-world and simulated scenes. The use of both
datasets boosts up the network performance, and the network architectures and
various loss functions in each phase help improve the image qualities. The
whole pipeline is trained in three phases. While each phase is mainly for one
of the three tasks, the networks in earlier phases are fine-tuned by respective
loss functions in an end-to-end manner. Experimental results show that EventSR
reconstructs high-quality SR images from events for both simulated and
real-world data.Comment: Accepted by CVPR 202