50 research outputs found
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
The AAU Multimodal Annotation Toolboxes: Annotating Objects in Images and Videos
This tech report gives an introduction to two annotation toolboxes that
enable the creation of pixel and polygon-based masks as well as bounding boxes
around objects of interest. Both toolboxes support the annotation of sequential
images in the RGB and thermal modalities. Each annotated object is assigned a
classification tag, a unique ID, and one or more optional meta data tags. The
toolboxes are written in C++ with the OpenCV and Qt libraries and are operated
by using the visual interface and the extensive range of keyboard shortcuts.
Pre-built binaries are available for Windows and MacOS and the tools can be
built from source under Linux as well. So far, tens of thousands of frames have
been annotated using the toolboxes.Comment: 6 pages, 10 figure
Is it Raining Outside? Detection of Rainfall using General-Purpose Surveillance Cameras
In integrated surveillance systems based on visual cameras, the mitigation of
adverse weather conditions is an active research topic. Within this field, rain
removal algorithms have been developed that artificially remove rain streaks
from images or video. In order to deploy such rain removal algorithms in a
surveillance setting, one must detect if rain is present in the scene. In this
paper, we design a system for the detection of rainfall by the use of
surveillance cameras. We reimplement the former state-of-the-art method for
rain detection and compare it against a modern CNN-based method by utilizing 3D
convolutions. The two methods are evaluated on our new AAU Visual Rain Dataset
(VIRADA) that consists of 215 hours of general-purpose surveillance video from
two traffic crossings. The results show that the proposed 3D CNN outperforms
the previous state-of-the-art method by a large margin on all metrics, for both
of the traffic crossings. Finally, it is shown that the choice of
region-of-interest has a large influence on performance when trying to
generalize the investigated methods. The AAU VIRADA dataset and our
implementation of the two rain detection algorithms are publicly available at
https://bitbucket.org/aauvap/aau-virada.Comment: 10 pages, 7 figures, CVPR2019 V4AS worksho