☆ 4.5 Article

Crowd density estimation based on multi scale features fusion network with reverse attention mechanism

APPLIED INTELLIGENCE (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Context-aware pyramid attention network for crowd counting

Lingyu Gu et al.

Summary: This study proposes an innovative Context-Aware Pyramid Attention Network for accurate crowd counting, which extracts rich contextual features and deals with dependencies on space and channels. Experimental results on multiple challenging datasets demonstrate significant improvement in counting accuracy with CAPAN, validating its effectiveness.

APPLIED INTELLIGENCE (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Crowd counting method based on the self-attention residual network

Yan-Bo Liu et al.

Summary: The crowd counting method proposed in this study utilizes multiscale convolution and self-attention residual modules to effectively address the issues of occlusion and scale changes in crowd estimation, ultimately improving the accuracy of crowd counting.

APPLIED INTELLIGENCE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A multi-scale and multi-level feature aggregation network for crowd counting

Fushun Zhu et al.

Summary: In this paper, a multi-scale and multi-level features aggregation network (MFANet) is proposed for accurate and efficient crowd counting. By introducing the scale and level aggregation module (SLAM) and normalized Euclidean loss (NEL), the network achieves state-of-the-art performance in crowd counting and localization, as demonstrated in extensive experiments on benchmark datasets.

NEUROCOMPUTING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Crowd counting with segmentation attention convolutional neural network

Jiwei Chen et al.

Summary: SegCrowdNet adaptsively highlights the human head region and suppresses the non-head region by segmentation, encodes highly refined density map with attention mechanism, intelligently classifies crowd count into groups, and learns multi-scale features to overcome scale variations.

IET IMAGE PROCESSING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

EDENet: Elaborate density estimation network for crowd counting

Yinfeng Xia et al.

Summary: In this paper, an end-to-end trainable architecture called EDENet is proposed to generate high-quality density estimation maps in the field of crowd counting. Extensive experiments demonstrate that EDENet can achieve state-of-the-art recognition performance and high robustness, with the density map having the highest Peak Signal to Noise Ratio (PSNR) considered to be of high quality.

NEUROCOMPUTING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A smartly simple way for joint crowd counting and localization

Minyang Jiang et al.

Summary: This study introduces a novel approach for crowd counting, which achieves both counting and localization tasks through a pedestrian-aware density map. Without the need for additional pretraining or fine-tuning, it provides a new direction for the field of crowd counting.

NEUROCOMPUTING (2021)

添加到收藏夹

Article Multidisciplinary Sciences

Convolutional Neural Network for Crowd Counting on Metro Platforms

Jun Zhang et al.

Summary: This paper addresses the issue of passenger flow detection on metro platforms and proposes a CNN-based network to accurately count people. Through experiments on a self-built dataset and standard datasets, the proposed method shows good performance and minimal estimation error.

SYMMETRY-BASEL (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

PDANet: Pyramid density-aware attention based network for accurate crowd counting

Saeed Amirgholipour et al.

Summary: This paper proposes a novel Pyramid Density-Aware Attention based network, PDANet, for density-aware crowd counting. By leveraging attention, pyramid scale feature, and two branch decoder modules, PDANet aims to extract features of different scales while focusing on relevant information and suppressing misleading information. It addresses the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules and achieves superior performance in accurate counting and generated density maps.

NEUROCOMPUTING (2021)

添加到收藏夹

Article Chemistry, Analytical

Congested Crowd Counting via Adaptive Multi-Scale Context Learning

Yani Zhang et al.

Summary: The proposed MSCANet network leverages spatial context information for crowd density estimation, showing compelling performance in challenging crowd counting benchmarks and proving effective in tasks such as crowd localization and remote sensing object counting.

SENSORS (2021)

添加到收藏夹

Article Computer Science, Information Systems

DENet: A Universal Network for Counting Crowd With Varying Densities and Scales

Lei Liu et al.

Summary: This paper introduces a network called DENet, consisting of a detection network and an encoder-decoder estimation network, for counting people or objects with varying scales and densities. Evaluation on three datasets shows that DENet achieves lower Mean Absolute Error (MAE) than state-of-the-art methods.

IEEE TRANSACTIONS ON MULTIMEDIA (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A classification method to detect faults in a rotating machinery based on kernelled support tensor machine and multilinear principal component analysis

Chaofan Hu et al.

Summary: A novel classification technique utilizing kernelled support tensor machine (KSTM) and multilinear principal component analysis (MPCA) is introduced for fault detection of rotating machines. Through the use of a 3-way tensor, KSTM, and MPCA, the technique effectively handles fault diagnosis and information classification in rotating machinery.

APPLIED INTELLIGENCE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Cross-domain intelligent fault classification of bearings based on tensor-aligned invariant subspace learning and two-dimensional convolutional neural networks

Chaofan Hu et al.

KNOWLEDGE-BASED SYSTEMS (2020)

添加到收藏夹

Article Computer Science, Information Systems

Crowd Counting Guided by Attention Network

Pei Nie et al.

INFORMATION (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism

Yuqian Zhang et al.

APPLIED SCIENCES-BASEL (2019)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Removing background interference for crowd counting via de-background detail convolutional network

Luyang Wang et al.

NEUROCOMPUTING (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

An Efficient Method of Crowd Aggregation Computation in Public Areas

Mingliang Xu et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Skip-connection convolutional neural network for still image crowd counting

Luyang Wang et al.

APPLIED INTELLIGENCE (2018)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Crowd Counting via Adversarial Cross-Scale Consistency Pursuit

Zan Shen et al.

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN

Deepak Babu Sam et al.

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)

添加到收藏夹

Article Engineering, Electrical & Electronic

Crowded Scene Analysis: A Survey

Teng Li et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Detecting Humans in Dense Crowds Using Locally-Consistent Scale Prior and Global Occlusion Reasoning

Haroon Idrees et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2015)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation

Viet-Quoc Pham et al.

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Counting People With Low-Level Features and Bayesian Regression

Antoni B. Chan et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2012)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Pedestrian Detection: An Evaluation of the State of the Art

Piotr Dollar et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2012)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Object Detection with Discriminatively Trained Part-Based Models

Pedro F. Felzenszwalb et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2010)

添加到收藏夹

Article Computer Science, Cybernetics

Estimation of number of people in crowded scenes using perspective transformation

SF Lin et al.

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.