20,218 research outputs found
Uncertainty Estimation in One-Stage Object Detection
Environment perception is the task for intelligent vehicles on which all
subsequent steps rely. A key part of perception is to safely detect other road
users such as vehicles, pedestrians, and cyclists. With modern deep learning
techniques huge progress was made over the last years in this field. However
such deep learning based object detection models cannot predict how certain
they are in their predictions, potentially hampering the performance of later
steps such as tracking or sensor fusion. We present a viable approaches to
estimate uncertainty in an one-stage object detector, while improving the
detection performance of the baseline approach. The proposed model is evaluated
on a large scale automotive pedestrian dataset. Experimental results show that
the uncertainty outputted by our system is coupled with detection accuracy and
the occlusion level of pedestrians
REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH
International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed
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 complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora
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