3,599 research outputs found
Recent advances in deep learning for object detection
Object detection is a fundamental visual recognition problem in computer
vision and has been widely studied in the past decades. Visual object detection
aims to find objects of certain target classes with precise localization in a
given image and assign each object instance a corresponding class label. Due to
the tremendous successes of deep learning based image classification, object
detection techniques using deep learning have been actively studied in recent
years. In this paper, we give a comprehensive survey of recent advances in
visual object detection with deep learning. By reviewing a large body of recent
related work in literature, we systematically analyze the existing object
detection frameworks and organize the survey into three major parts: (i)
detection components, (ii) learning strategies, and (iii) applications &
benchmarks. In the survey, we cover a variety of factors affecting the
detection performance in detail, such as detector architectures, feature
learning, proposal generation, sampling strategies, etc. Finally, we discuss
several future directions to facilitate and spur future research for visual
object detection with deep learning. Keywords: Object Detection, Deep Learning,
Deep Convolutional Neural Network
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
Fast Automatic Vehicle Annotation for Urban Traffic Surveillance
Automatic vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for intelligent transportation systems. In this paper, we present a fast algorithm: detection and annotation for vehicles (DAVE), which effectively combines vehicle detection and attributes annotation into a unified framework. DAVE consists of two convolutional neural networks: a shallow fully convolutional fast vehicle proposal network (FVPN) for extracting all vehicles' positions, and a deep attributes learning network (ALN), which aims to verify each detection candidate and infer each vehicle's pose, color, and type information simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the deep empirical ALN can be exploited to guide training the much simpler FVPN. Once the system is trained, DAVE can achieve efficient vehicle detection and attributes annotation for real-world traffic surveillance data, while the FVPN can be independently adopted as a real-time high-performance vehicle detector as well. We evaluate the DAVE on a new self-collected urban traffic surveillance data set and the public PASCAL VOC2007 car and LISA 2010 data sets, with consistent improvements over existing algorithms
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