OmniHuman is an end-to-end AI framework developed by researchers at ByteDance. It can generate incredibly realistic human videos from just a single image and a motion signal—like audio or video.

In this paper, we present a novel one-dimensional (1D) CNN HAR that utilizes a divide and conquer-based classifier learning with test data sharpening for improving HAR.

2021年4月1日 · To address this issue, 1D CNNs have recently been proposed and immediately achieved the state-of-the-art performance levels in several applications such as personalized biomedical data classification and early diagnosis, structural health monitoring, anomaly detection and identification in power electronics and electrical motor fault detection.

Our goal is classify human activities from sensor measurements with as little data engineering as possible. We normalize each individual measurement reading with respect to readings in the same category across the dataset, and concatenate a reading at a single timestep into a 40 x 1 feature vector to be used as input.

2021年6月1日 · A novel method of human activity recognition is proposed in this paper (Xu et al., 2017), where accelerometer data is gathered from wearable devices and used as input for a random forest (RF) model to categorize human activity. Finally, they reached an average accuracy of 90% for going upstairs, walking and maintaining static recognition, 80% ...

How to load and prepare the data for a standard human activity recognition dataset and develop a single 1D CNN model that achieves excellent performance on the raw data. How to further tune the performance of the model, including data transformation, filter maps, and kernel sizes.

The paper aims to show an approach for detecting human interactions in videos, which utilizes several different methods - YOLOv5 for object detection, CSR-DCF and Kalman Filter for object tracking, and ID Convolutional Neural Network (1D-CNN) for real-time interaction detection.

2021年10月26日 · In this paper, we advise a 1D CNN approach to human hobby recognition that reasons to decorate interest popularity efficiency by leveraging a CNN network’s robustness in feature extraction. This look centered on the guidance, trying out, and assessment of a selection of physical games.

2021年10月7日 · Human activity recognition is an important research field with a variety of applications in healthcare monitoring, fitness tracking and in user-adaptive systems in smart environments. The problem of human activity recognition can be solved using a 1D convolutional neural network (CNN) trained with accelerometric data.

Human Activity Recognition. Contribute to KennCoder7/HAR-CNN-1d development by creating an account on GitHub.