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Yu (Brian) Yao's Projects

jetson-detectors icon jetson-detectors

Contains examples and documentation on how to setup your remote development environment from your Windows host to a Jetson device

jetson-inference icon jetson-inference

Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

keras-ncp icon keras-ncp

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

maskrcnn-benchmark icon maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mdp_tracking icon mdp_tracking

Learning to Track: Online Multi-Object Tracking by Decision Making

openpose icon openpose

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

osm_database icon osm_database

Codes to connect to an osm database file and query road information

piepredict icon piepredict

PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction

sfmlearner icon sfmlearner

An unsupervised learning framework for depth and ego-motion estimation from monocular videos

sim_traj icon sim_traj

Code for Stochastic Sampling Simulation paper (https://ieeexplore.ieee.org/document/8967857)

sort icon sort

Simple, online, and realtime tracking of multiple objects in a video sequence.

star icon star

Code for "Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction"

sticky-hdp-hmm icon sticky-hdp-hmm

This is forked from yunjhongwu's github: https://github.com/yunjhongwu/Sticky-HDPHMM-demo

tad-iros2019 icon tad-iros2019

Code of the Unsupervised Traffic Accident Detection paper in Pytorch.

trajectron-plus-plus icon trajectron-plus-plus

Code accompanying "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (* denotes equal contribution).

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