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.

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

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

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

Panoramic Human Activity Recognition, in ECCV 2022.

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

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 hierarchical deep temporal models for group activity recognition

Reproduced "Hierarchical Deep Temporal Models for Group Activity Recognition" paper.

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.

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

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