5,873 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
Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation
Although convolutional neural networks (CNNs) have been proposed to remove
adverse weather conditions in single images using a single set of pre-trained
weights, they fail to restore weather videos due to the absence of temporal
information. Furthermore, existing methods for removing adverse weather
conditions (e.g., rain, fog, and snow) from videos can only handle one type of
adverse weather. In this work, we propose the first framework for restoring
videos from all adverse weather conditions by developing a video
adverse-weather-component suppression network (ViWS-Net). To achieve this, we
first devise a weather-agnostic video transformer encoder with multiple
transformer stages. Moreover, we design a long short-term temporal modeling
mechanism for weather messenger to early fuse input adjacent video frames and
learn weather-specific information. We further introduce a weather
discriminator with gradient reversion, to maintain the weather-invariant common
information and suppress the weather-specific information in pixel features, by
adversarially predicting weather types. Finally, we develop a messenger-driven
video transformer decoder to retrieve the residual weather-specific feature,
which is spatiotemporally aggregated with hierarchical pixel features and
refined to predict the clean target frame of input videos. Experimental
results, on benchmark datasets and real-world weather videos, demonstrate that
our ViWS-Net outperforms current state-of-the-art methods in terms of restoring
videos degraded by any weather condition
MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing
Image restoration of snow scenes in severe weather is a difficult task. Snow
images have complex degradations and are cluttered over clean images, changing
the distribution of clean images. The previous methods based on CNNs are
challenging to remove perfectly in restoring snow scenes due to their local
inductive biases' lack of a specific global modeling ability. In this paper, we
apply the vision transformer to the task of snow removal from a single image.
Specifically, we propose a parallel network architecture split along the
channel, performing local feature refinement and global information modeling
separately. We utilize a channel shuffle operation to combine their respective
strengths to enhance network performance. Second, we propose the MSP module,
which utilizes multi-scale avgpool to aggregate information of different sizes
and simultaneously performs multi-scale projection self-attention on multi-head
self-attention to improve the representation ability of the model under
different scale degradations. Finally, we design a lightweight and simple local
capture module, which can refine the local capture capability of the model.
In the experimental part, we conduct extensive experiments to demonstrate the
superiority of our method. We compared the previous snow removal methods on
three snow scene datasets. The experimental results show that our method
surpasses the state-of-the-art methods with fewer parameters and computation.
We achieve substantial growth by 1.99dB and SSIM 0.03 on the CSD test dataset.
On the SRRS and Snow100K datasets, we also increased PSNR by 2.47dB and 1.62dB
compared with the Transweather approach and improved by 0.03 in SSIM. In the
visual comparison section, our MSP-Former also achieves better visual effects
than existing methods, proving the usability of our method
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