Journal ArticleDOI
Benchmarking a large-scale FIR dataset for on-road pedestrian detection
TLDR
A nighttime FIR pedestrian dataset with the largest scale at present is introduced in this paper, which is called SCUT (South China University of Technology) dataset and shows that convolutional neural networks (CNN) based detectors obtained good performance on FIR image.About:
This article is published in Infrared Physics & Technology.The article was published on 2019-01-01. It has received 41 citations till now. The article focuses on the topics: Pedestrian detection.read more
Citations
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Journal ArticleDOI
Pedestrian Detection in Severe Weather Conditions
TL;DR: A 16-bit thermal data dataset called ZUT (Zachodniopomorski Uniwersytet Technologiczny) is introduced as having the widest variety of fine-grained annotated images captured in the four biggest European Union countries captured during severe weather conditions.
Journal ArticleDOI
Pedestrian detection using a moving camera: A novel framework for foreground detection
TL;DR: A novel trajectory classification framework for detecting pedestrians even in challenging real-world environments is proposed and experimental results confirm the effectiveness of the proposed approach in capturing the dynamic aspect between frames and therefore detecting the presence of pedestrians in the scene.
Journal ArticleDOI
Pedestrian Street-Cross Action Recognition in Monocular Far Infrared Sequences
TL;DR: In this paper, a complete and original model for assessing if a pedestrian is engaged in a street cross action using only infrared monocular scene perception is proposed, which is done by the time-series analysis of features like: pedestrian motion, position of pedestrians with respect to the drivable area and their distance to the ego-vehicle.
Journal ArticleDOI
Object detection from UAV thermal infrared images and videos using YOLO models
Chenchen Jiang,Huazhong Ren,Xin Ye,Jinshun Zhu,Hui Zeng,Yang Nan,Min Sun,Xiang Ren,Hongtao Huo +8 more
TL;DR: In this paper , a You Only Look Once (YOLO) model based on CNN architecture was proposed for object detection in UAV-based thermal infrared (TIR) images and videos, which were captured by forward-looking infrared (FLIR) cameras.
Journal ArticleDOI
MCNet: Multi-level Correction Network for thermal image semantic segmentation of nighttime driving scene
TL;DR: A multi-level correction network (MCNet) with amulti-level attention module (MAM), a multi- level edge enhancement module (MEEM), and a new dataset called SCUT-Seg which contains 2010 thermal images recorded from various road scenes with 10 manually annotated semantic region labels are presented.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
You Only Look Once: Unified, Real-Time Object Detection
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.