726 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
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks
People often use a web search engine to find information about events of
interest, for example, sport competitions, political elections, festivals and
entertainment news. In this paper, we study a problem of detecting
event-related queries, which is the first step before selecting a suitable
time-aware retrieval model. In general, event-related information needs can be
observed in query streams through various temporal patterns of user search
behavior, e.g., spiky peaks for popular events, and periodicities for
repetitive events. However, it is also common that users search for non-popular
events, which may not exhibit temporal variations in query streams, e.g., past
events recently occurred, historical events triggered by anniversaries or
similar events, and future events anticipated to happen. To address the
challenge of detecting dynamic classes of events, we propose a novel deep
learning model to classify a given query into a predetermined set of multiple
event types. Our proposed model, a Stacked Multilayer Perceptron (S-MLP)
network, consists of multilayer perceptron used as a basic learning unit. We
assemble stacked units to further learn complex relationships between neutrons
in successive layers. To evaluate our proposed model, we conduct experiments
using real-world queries and a set of manually created ground truth.
Preliminary results have shown that our proposed deep learning model
outperforms the state-of-the-art classification models significantly.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, 6 pages, 4
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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
A comprehensive study of sparse codes on abnormality detection
Sparse representation has been applied successfully in abnormal event
detection, in which the baseline is to learn a dictionary accompanied by sparse
codes. While much emphasis is put on discriminative dictionary construction,
there are no comparative studies of sparse codes regarding abnormality
detection. We comprehensively study two types of sparse codes solutions -
greedy algorithms and convex L1-norm solutions - and their impact on
abnormality detection performance. We also propose our framework of combining
sparse codes with different detection methods. Our comparative experiments are
carried out from various angles to better understand the applicability of
sparse codes, including computation time, reconstruction error, sparsity,
detection accuracy, and their performance combining various detection methods.
Experiments show that combining OMP codes with maximum coordinate detection
could achieve state-of-the-art performance on the UCSD dataset [14].Comment: 7 page
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