4.6 Article

Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting

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 Chemistry, Multidisciplinary

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

Hyun-Ki Jung et al.

Summary: This study explores the application of object detection technology by improving the YOLOv5 model and comparing experimental data to determine the best model for object detection under various environmental conditions.

APPLIED SCIENCES-BASEL (2022)

Article Computer Science, Artificial Intelligence

A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications

Preksha Pareek et al.

Summary: This paper discusses various machine learning and deep learning techniques used for Human Action Recognition (HAR) from 2011 to 2019, investigates the characteristics of public datasets, and introduces various action recognition techniques and their applications.

ARTIFICIAL INTELLIGENCE REVIEW (2021)

Article Computer Science, Artificial Intelligence

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

Pritul Dave et al.

Summary: The global issue of traffic congestion and increasing number of vehicles has led to the development of a smart traffic management algorithm using Internet of Things and deep learning techniques, which effectively reduces waiting time and fuel consumption.

PEERJ COMPUTER SCIENCE (2021)

Article Computer Science, Artificial Intelligence

Skeleton-based action recognition using sparse spatio-temporal GCN with edge effective resistance

Tasweer Ahmad et al.

Summary: This paper introduces techniques of graph sparsification and self-attention graph pooling to address issues in skeleton-based action recognition, achieving state-of-the-art results.

NEUROCOMPUTING (2021)

Article Computer Science, Artificial Intelligence

Human action recognition in drone videos using a few aerial training examples

Waqas Sultani et al.

Summary: This paper discusses a method to improve aerial action classification using video games and Generative Adversarial Networks as data sources, demonstrating the effectiveness of this approach when only a few real aerial training examples are available.

COMPUTER VISION AND IMAGE UNDERSTANDING (2021)

Article Chemistry, Analytical

UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective

Mingjie Liu et al.

SENSORS (2020)

Article Computer Science, Artificial Intelligence

Human activity recognition from UAV-captured video sequences

Hazar Mliki et al.

PATTERN RECOGNITION (2020)

Article Computer Science, Artificial Intelligence

Deep learning-based object detection in low-altitude UAV datasets: A survey

Payal Mittal et al.

IMAGE AND VISION COMPUTING (2020)

Article Engineering, Multidisciplinary

An efficient human action recognition framework with pose-based spatiotemporal features

Saeid Agahian et al.

ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH (2020)

Article Computer Science, Information Systems

UAV-Based Situational Awareness System Using Deep Learning

Ruben Geraldes et al.

IEEE ACCESS (2019)

Proceedings Paper Computer Science, Theory & Methods

Action Recognition in Still Images using Residual Neural Network Features

S. R. Sreela et al.

8TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2018) (2018)

Article Computer Science, Artificial Intelligence

Evaluation of video activity localizations integrating quality and quantity measurements

Christian Wolf et al.

COMPUTER VISION AND IMAGE UNDERSTANDING (2014)

Article Computer Science, Artificial Intelligence

Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning

Saad Ali et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2010)

Article Computer Science, Interdisciplinary Applications

Stochastic gradient boosting

JH Friedman

COMPUTATIONAL STATISTICS & DATA ANALYSIS (2002)

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)