We propose a new dataset taken by fisheye cameras for 3D HPE and HAR. We compare and analyze the existing methods on public and our datasets. We outline the challenge and future perspectives. 3D human pose estimation based on visual information aims to predict 3D poses of humans in images or videos.

Apr 30, 2025 · Existing methods mainly focus on 3D human pose estimation and human action recognition using images/videos recorded by perspective cameras. In contrast to perspective cameras, fisheye cameras use wide-angle lenses capturing wider field-of-views (FOV). Fisheye cameras are used in many applications such as surveillance and autonomous driving.

Feb 15, 2022 · In this paper, we decode human body pose and implement MediaPipe Holistic, a solution provided by MediaPipe ML framework, made up of upto eight different models that coordinate with each other in real time while minimizing memory transfer to provide pose estimation, face detection, and hand tracking into a single efficient end-to-end pipeline [7...

Mar 25, 2020 · We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB sensors using simple cameras. The approach proceeds along two stages. In the first, a real-time 2D pose detector is run to determine the precise pixel location of important keypoints of the human body.

May 5, 2023 · In this article, Face Recognition with Local Binary Patterns (LBPs) and OpenCV is discussed. Let's start with understanding the logic behind performing face recognition using LBPs. A beginner-friendly explanation of LBPs is described below.

May 30, 2024 · Human activity recognition (HAR) identifies and classifies patterns in image data that represent human activity. It is an essential problem in many fields, such as health care, where it can...

6 days ago · In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 525–534 (2021). Korkalo, O. Systems and methods for multiple-view and depth-based people tracking and human ...

Dec 1, 2019 · As RGB cameras responds easily to illumination changes and surrounding clutters, the worthwhile RGB Depth (RGB-D) camera sensors (e.g. Kinect) is used to improve the action recognition. This paper aims at classifying Human Actions by integrating salient motion features from both RGB and Depth Camera.

In this work, efficient human activity recognition (HAR) algorithm based on deep learning architecture is proposed to classify activities into seven different classes.

Human action recognition is an important topic in computer vision with its applications in robotics, video surveillance, human-computer interaction, user interface design, and multimedia...