Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

A selection of state-of-the-art research materials on trajectory prediction

A list of Human-Object Interaction Learning.

A deep learning framework for multi-animal pose tracking.

a toolkit for pose estimation using deep learning

Owlyshield is an EDR framework designed to safeguard vulnerable applications from potential exploitation (C&C, exfiltration and impact).

Behavioral segmentation of open field in DeepLabCut, or B-SOID ("B-side"), is a pipeline that pairs unsupervised pattern recognition with supervised classification to achieve fast predictions of behaviors that are not predefined by users.

Variational Animal Motion Embedding - A tool for time series embedding and clustering

Behavioral Observation Research Interactive Software

Motion Planner for Self Driving Cars

Various scripts to support deeplabcut and what to do afterwards!

Open-source datasets for anyone interested in working with network anomaly based machine learning, data science and research

live, low-latency markerless multi-camera 3D animal tracking system

Use iterative feature pruning to identify hierarchical clusters.

Closed-loop behavioral experiment toolkit using pose estimation of body parts.

JS Library for user behaviour tracking from the browser, using mouse movements, clicks, scroll, and time on page.

WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans

Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs

SIPEC: the deep-learning Swiss knife for behavioral data analysis

Crowd behavior analysis is an important field of research in modern world. It has wide applications in surveillance and public safety which are one of the prime social concerns. One way to analyze crowd behavior is obtain crowd movement data and then find out outliers in the individual trajectories to infer any abnormal behavior in the crowd.