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Human action classification system with pose-based (MediaPipe) and video-based (3D CNN) models. Features 100+ architectures for real-time pose classification and temporal models pretrained on UCF-101/HMDB51. Applications include autonomous vehicles, video surveillance, and action recognition research.
This repository contains the code implementation used in the paper Temporally Coherent Embeddings for Self-Supervised Video Representation Learning (TCE).
This repository contains my personal code for the paper Learning Spatiotemporal Features with 3D Convolutional Networks by Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri.
This is the project repository for the research study "Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recognition" presented by Motasem S. Alsawadi and Miguel Rio.
This project builds a video classification model using CNNs for spatial feature extraction and RNNs for temporal sequence modeling. Utilizing the UCF101 dataset, it covers data preprocessing, feature extraction, model training, and evaluation, providing a comprehensive approach to action recognition in videos.
This repository dedicated to Adaptive Deep Learning for Environment-Agnostic Human Action Recognition. This project focuses on developing a robust deep learning system tailored for accurate identification and analysis of human actions across diverse environments, with applications spanning surveillance, security, sports, and fitness.