Applying Deep Learning Approaches to Volleyball Data

[ICCV 2021] A new codebase containing various methods for Group Activity Recognition. Paper title: Spatio-Temporal Dynamic Inference Network for Group Activity Recognition.

A curated list of Group Activity (a.k.a., Collective Activity) recognition papers, codes, datasets and other resources.

The official repository of the paper "SGA-INTERACT: A 3D Skeleton-based Benchmark for Group Activity Understanding in Modern Basketball Tactic"

[NeurIPS 2024] CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition

Panoramic Human Activity Recognition, in ECCV 2022.

A modern implementation of CVPR 2016 paper: "A Hierarchical Deep Temporal Model for Group Activity Recognition."

Modern implementation of CVPR16 "Hierarchical Deep Temporal Models for Group Activity Recognition" paper

Enhanced adaptation of MP-GCN for recognizing playground activities using only 2D skeletons and geometric cues. It builds spatio-temporal graphs that capture interactions among people and playground structures, supported by a streamlined pipeline for pose detection, tracking, graph construction, data stratification, and multi-stream training

PyTorch implementation of a Relational Attention-Based Model for Group Activity Recognition — inspired by "A Hierarchical Deep Temporal Model for Group Activity Recognition (arXiv:1511.06040v2)"

2020 CVPR Workshop Paper -- RIT-18: A Novel Dataset for Compositional Group Activity Understanding

A modern PyTorch implementation of the CVPR 2016 paper "Hierarchical Deep Temporal Models for Group Activity Recognition"

A new paper implementaion "Hierarchical Relational Networks for Group Activity Recognition and Retrieval"

A modern PyTorch implementation of hierarchical deep temporal models for group activity recognition

▎ PyTorch implementation of group activity recognition on the CVPR 2016 volleyball dataset — 8 baselines from single-frame CNNs to hierarchical LSTMs, with a unified data loader, Hydra configs, and Kaggle-ready training.

This project implements a hierarchical deep learning approach for recognizing group activities in videos. The core idea is to model both individual person actions and group-level activities using temporal dynamics captured by LSTM networks.

Modern & enhanced implementation of Hierarchical Deep Temporal Models for Group Activity Recognition. Achieves 90.2% accuracy on the Volleyball Dataset using Global-Local Feature Fusion.

A modern pytorch implementation of CVPR 2016 paper: "A Hierarchical Deep Temporal Model for Group Activity Recognition."

A modern implementation of ECCV 2018 paper: "Hierarchical Relational Networks for Group Activity Recognition and Retrieval".

Implementation of the CVPR 2016 paper, “A Hierarchical Deep Temporal Model for Group Activity Recognition”