2,397 research outputs found
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.Comment: ACCV 201
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
MOR-UAV: A Benchmark Dataset and Baselines for Moving Object Recognition in UAV Videos
Visual data collected from Unmanned Aerial Vehicles (UAVs) has opened a new
frontier of computer vision that requires automated analysis of aerial
images/videos. However, the existing UAV datasets primarily focus on object
detection. An object detector does not differentiate between the moving and
non-moving objects. Given a real-time UAV video stream, how can we both
localize and classify the moving objects, i.e. perform moving object
recognition (MOR)? The MOR is one of the essential tasks to support various UAV
vision-based applications including aerial surveillance, search and rescue,
event recognition, urban and rural scene understanding.To the best of our
knowledge, no labeled dataset is available for MOR evaluation in UAV videos.
Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for
MOR in aerial videos. We achieve this by labeling axis-aligned bounding boxes
for moving objects which requires less computational resources than producing
pixel-level estimates. We annotate 89,783 moving object instances collected
from 30 UAV videos, consisting of 10,948 frames in various scenarios such as
weather conditions, occlusion, changing flying altitude and multiple camera
views. We assigned the labels for two categories of vehicles (car and heavy
vehicle). Furthermore, we propose a deep unified framework MOR-UAVNet for MOR
in UAV videos. Since, this is a first attempt for MOR in UAV videos, we present
16 baseline results based on the proposed framework over the MOR-UAV dataset
through quantitative and qualitative experiments. We also analyze the
motion-salient regions in the network through multiple layer visualizations.
The MOR-UAVNet works online at inference as it requires only few past frames.
Moreover, it doesn't require predefined target initialization from user.
Experiments also demonstrate that the MOR-UAV dataset is quite challenging
Constrained Distance Based Clustering for Satellite Image Time-Series
International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018
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