1,014 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
DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection
Although YOLOv2 approach is extremely fast on object detection; its backbone
network has the low ability on feature extraction and fails to make full use of
multi-scale local region features, which restricts the improvement of object
detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense
Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating
the object detection accuracy of YOLOv2. Specifically, the dense connection of
convolution layers is employed in the backbone network of YOLOv2 to strengthen
the feature extraction and alleviate the vanishing-gradient problem. Moreover,
an improved spatial pyramid pooling is introduced to pool and concatenate the
multi-scale local region features, so that the network can learn the object
features more comprehensively. The DC-SPP-YOLO model is established and trained
based on a new loss function composed of mean square error and cross entropy,
and the object detection is realized. Experiments demonstrate that the mAP
(mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and
UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy
of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and
using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table
Unsupervised learning of object landmarks by factorized spatial embeddings
Learning automatically the structure of object categories remains an
important open problem in computer vision. In this paper, we propose a novel
unsupervised approach that can discover and learn landmarks in object
categories, thus characterizing their structure. Our approach is based on
factorizing image deformations, as induced by a viewpoint change or an object
deformation, by learning a deep neural network that detects landmarks
consistently with such visual effects. Furthermore, we show that the learned
landmarks establish meaningful correspondences between different object
instances in a category without having to impose this requirement explicitly.
We assess the method qualitatively on a variety of object types, natural and
man-made. We also show that our unsupervised landmarks are highly predictive of
manually-annotated landmarks in face benchmark datasets, and can be used to
regress these with a high degree of accuracy.Comment: To be published in ICCV 201
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
Advances and Applications of Computer Vision Techniques in Vehicle Trajectory Generation and Surrogate Traffic Safety Indicators
The application of Computer Vision (CV) techniques massively stimulates
microscopic traffic safety analysis from the perspective of traffic conflicts
and near misses, which is usually measured using Surrogate Safety Measures
(SSM). However, as video processing and traffic safety modeling are two
separate research domains and few research have focused on systematically
bridging the gap between them, it is necessary to provide transportation
researchers and practitioners with corresponding guidance. With this aim in
mind, this paper focuses on reviewing the applications of CV techniques in
traffic safety modeling using SSM and suggesting the best way forward. The CV
algorithm that are used for vehicle detection and tracking from early
approaches to the state-of-the-art models are summarized at a high level. Then,
the video pre-processing and post-processing techniques for vehicle trajectory
extraction are introduced. A detailed review of SSMs for vehicle trajectory
data along with their application on traffic safety analysis is presented.
Finally, practical issues in traffic video processing and SSM-based safety
analysis are discussed, and the available or potential solutions are provided.
This review is expected to assist transportation researchers and engineers with
the selection of suitable CV techniques for video processing, and the usage of
SSMs for various traffic safety research objectives
FlashLight: A Dynamic Detector of Shared State, Race Conditions, and Locking Models in Concurrent Java Programs
Concurrent Java programs are difficult to understand and implement correctly. This difficultly leads to code faults that are the source of many real-world reliability and security problems. Many factors contribute to concurrency faults in Java code; for example, programmers may not understand Java language semantics or, when using a Java library or framework, may not understand that their resulting program is concurrent. This thesis describes a dynamic analysis tool, named FlashLight, that detects shared state and possible race conditions within a program. FlashLight illuminates the concurrency within a program for programmers who are wholly or partially in the dark about their software\u27s concurrency. FlashLight also works in concert with the Fluid assurance tool to propose Greenhouse-style lock policy models based on a program\u27s observed locking behavior. After review by a programmer to ensure reasonableness, these models can be verified by the Fluid assurance tool. The author\u27s combination of a dynamic tool with a program verification system focused on concurrency fault detection and repair is the primary contribution of this research. He applied FlashLight to several concurrent Java programs, including a large commercial web application server. His case study experiences induced him to improve FlashLight to do the following: (1) allow the programmer to specify interesting time quantums (e.g., this is the start-up phase of the program), and (2) support the common Java programming idiom of not locking shared state during object construction. Both improvements help to reduce false positives. FlashLight introduces an overhead of roughly 1.7 times the original execution time of the program. The most significant limitation of FlashLight is that it is not fully integrated into the Fluid assurance tool with respect to the user experience
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