687 research outputs found
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed
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
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras : A Comparison
Altres ajuts: DGT (SPIP2014-01352)Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and nighttime. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images; (b) just infrared images; and (c) both of them. In order to obtain results for the last item, we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset that we have built for this purpose as well as on the publicly available KAIST multispectral dataset
Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images
This paper proposes a feature-based technique to detect pedestrians and recognize vehicles within thermal images that have been captured during nighttime. The proposed technique applies the support vector machine (SVM) classifier on CENsus Transformed histogRam Oriented Gradient (CENTROG) features in order to classify and detect humans and/or vehicles. Although thermal images suffer from low image resolution, lack of colour and poor texture information, they offer the advantage of being unaffected by high intensity light sources such as vehicle headlights which tend to render normal images unsuitable for nighttime image capturing and subsequent analysis. Since contour is the most distinctive feature within thermal images, CENTROG is used to capture this feature information and is used within the experiments. The experimental results so obtained were compared with those obtained by employing the CENsus TRansformed hISTogram (CENTRIST). Experimental results revealed that CENTROG offers better detection and classification accuracy for both pedestrian and detection and vehicle type recognition
Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges
Pedestrian detection has become a cornerstone for several high-level tasks,
including autonomous driving, intelligent transportation, and traffic
surveillance. There are several works focussed on pedestrian detection using
visible images, mainly in the daytime. However, this task is very intriguing
when the environmental conditions change to poor lighting or nighttime.
Recently, new ideas have been spurred to use alternative sources, such as Far
InfraRed (FIR) temperature sensor feeds for detecting pedestrians in low-light
conditions. This study comprehensively reviews recent developments in low-light
pedestrian detection approaches. It systematically categorizes and analyses
various algorithms from region-based to non-region-based and graph-based
learning methodologies by highlighting their methodologies, implementation
issues, and challenges. It also outlines the key benchmark datasets that can be
used for research and development of advanced pedestrian detection algorithms,
particularly in low-light situation
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