793 research outputs found
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
Robust Correlation Tracking for UAV Videos via Feature Fusion and Saliency Proposals
Following the growing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on object tracking using videos recorded from UAVs. However, tracking from UAV videos poses many challenges due to platform motion, including background clutter, occlusion, and illumination variation. This paper tackles these challenges by proposing a correlation filter-based tracker with feature fusion and saliency proposals. First, we integrate multiple feature types such as dimensionality-reduced color name (CN) and histograms of oriented gradient (HOG) features to improve the performance of correlation filters for UAV videos. Yet, a fused feature acting as a multivector descriptor cannot be directly used in prior correlation filters. Therefore, a fused feature correlation filter is proposed that can directly convolve with a multivector descriptor, in order to obtain a single-channel response that indicates the location of an object. Furthermore, we introduce saliency proposals as re-detector to reduce background interference caused by occlusion or any distracter. Finally, an adaptive template-update strategy according to saliency information is utilized to alleviate possible model drifts. Systematic comparative evaluations performed on two popular UAV datasets show the effectiveness of the proposed approach
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
Remote sensing object detection (RSOD), one of the most fundamental and
challenging tasks in the remote sensing field, has received longstanding
attention. In recent years, deep learning techniques have demonstrated robust
feature representation capabilities and led to a big leap in the development of
RSOD techniques. In this era of rapid technical evolution, this review aims to
present a comprehensive review of the recent achievements in deep learning
based RSOD methods. More than 300 papers are covered in this review. We
identify five main challenges in RSOD, including multi-scale object detection,
rotated object detection, weak object detection, tiny object detection, and
object detection with limited supervision, and systematically review the
corresponding methods developed in a hierarchical division manner. We also
review the widely used benchmark datasets and evaluation metrics within the
field of RSOD, as well as the application scenarios for RSOD. Future research
directions are provided for further promoting the research in RSOD.Comment: Accepted with IEEE Geoscience and Remote Sensing Magazine. More than
300 papers relevant to the RSOD filed were reviewed in this surve
An Evaluation of Deep Learning-Based Object Identification
Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study
A Survey of Deep Learning-Based Object Detection
Object detection is one of the most important and challenging branches of
computer vision, which has been widely applied in peoples life, such as
monitoring security, autonomous driving and so on, with the purpose of locating
instances of semantic objects of a certain class. With the rapid development of
deep learning networks for detection tasks, the performance of object detectors
has been greatly improved. In order to understand the main development status
of object detection pipeline, thoroughly and deeply, in this survey, we first
analyze the methods of existing typical detection models and describe the
benchmark datasets. Afterwards and primarily, we provide a comprehensive
overview of a variety of object detection methods in a systematic manner,
covering the one-stage and two-stage detectors. Moreover, we list the
traditional and new applications. Some representative branches of object
detection are analyzed as well. Finally, we discuss the architecture of
exploiting these object detection methods to build an effective and efficient
system and point out a set of development trends to better follow the
state-of-the-art algorithms and further research.Comment: 30 pages,12 figure
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
Machine learning methods for discriminating natural targets in seabed imagery
The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems.
These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation.
Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture
classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world
sonar mosaic imagery.
A number of technical challenges arose and these were
surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation
of pockmark and Sabellaria discrimination is feasible within this framework
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