This project is designed to display how we can utilize deep learning methods for Sports Data Analytics.

[NeurIPS 2022 Spotlight] VideoMAE for Action Detection

[NeurIPS'22] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

EchoCardMAE: Video Masked Auto-Encoders Customized for Echocardiography

Real-time SL detection system featuring WebSocket streaming, VideoMAE fine-tuned on WLASL, and LLM-driven semantic disambiguation. Includes a full pipeline for 3D motion capture (JSON/Three.js) and optimized inference for CUDA and DirectML.

Real‑time crime detection system using VideoMAE, Flask, async camera streaming, alert snapshots, and email notifications.

International Contest on Illegal Waste Dumping Detection

A Computer Vision Project for the AAI3001 module

Deep learning–based anomaly detection in egocentric traffic videos using ConvAE reconstruction and VideoMAE transformer classification.

Chitr professional case-study showcase (documentation only, no source code)

Efficient video action recognition using hybrid techniques: combining ORB, SIFT, and deep models like VideoMAE and (2+1)D Conv to reduce data size while maintaining performance.

Video action recognition using VideoMAE and SlowFast with temporal modeling

Edge-Optimized CCTV Anomaly Detection using VideoMAE.