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. Over the past two decades, we have …
vision, has received great attention in recent years. Over the past two decades, we have …
Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review
Object detection is a fundamental but challenging issue in the field of generic image
analysis; it plays an important role in a wide range of applications and has been receiving …
analysis; it plays an important role in a wide range of applications and has been receiving …
Deep learning for generic object detection: A survey
Object detection, one of the most fundamental and challenging problems in computer vision,
seeks to locate object instances from a large number of predefined categories in natural …
seeks to locate object instances from a large number of predefined categories in natural …
Application of deep learning for object detection
The ubiquitous and wide applications like scene understanding, video surveillance, robotics,
and self-driving systems triggered vast research in the domain of computer vision in the most …
and self-driving systems triggered vast research in the domain of computer vision in the most …
Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection
The performance of object detection has recently been significantly improved due to the
powerful features learnt through convolutional neural networks (CNNs). Despite the …
powerful features learnt through convolutional neural networks (CNNs). Despite the …
Temporal action localization in untrimmed videos via multi-stage cnns
We address temporal action localization in untrimmed long videos. This is important
because videos in real applications are usually unconstrained and contain multiple action …
because videos in real applications are usually unconstrained and contain multiple action …
Trunk-branch ensemble convolutional neural networks for video-based face recognition
C Ding, D Tao - IEEE transactions on pattern analysis and …, 2017 - ieeexplore.ieee.org
Human faces in surveillance videos often suffer from severe image blur, dramatic pose
variations, and occlusion. In this paper, we propose a comprehensive framework based on …
variations, and occlusion. In this paper, we propose a comprehensive framework based on …
Multi-scale object detection in remote sensing imagery with convolutional neural networks
Z Deng, H Sun, S Zhou, J Zhao, L Lei, H Zou - ISPRS journal of …, 2018 - Elsevier
Automatic detection of multi-class objects in remote sensing images is a fundamental but
challenging problem faced for remote sensing image analysis. Traditional methods are …
challenging problem faced for remote sensing image analysis. Traditional methods are …
Deeply learned compositional models for human pose estimation
Compositional models represent patterns with hierarchies of meaningful parts and subparts.
Their ability to characterize high-order relationships among body parts helps resolve low …
Their ability to characterize high-order relationships among body parts helps resolve low …
End-to-end people detection in crowded scenes
R Stewart, M Andriluka, AY Ng - Proceedings of the IEEE …, 2016 - cv-foundation.org
Current people detectors operate either by scanning an image in a sliding window fashion
or by classifying a discrete set of proposals. We propose a model that is based on decoding …
or by classifying a discrete set of proposals. We propose a model that is based on decoding …