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-deep-automotive-perception-

2D tracking; 3D detection; Lidar-Vision based Odometry; multi-agent interaction

.tmux

🇫🇷 Oh My Tmux! Pretty & versatile tmux configuration made with ❤️ (imho the best tmux configuration that just works)

3d-deepbox

3D Bounding Box Estimation Using Deep Learning and Geometry (MultiBin)

3d-vehicle-tracking

Official implementation of Joint Monocular 3D Detection and Tracking (ICCV 2019)

3d_object_detection

3D object detection for autonomous driving, e.g. car, pedestrian, cyclist

3dmot

568-final-project

Final Project for EECS/NAVARCH 568 (Mobile Robotics)

ab3dmot

Official Python Implementation for "A Baseline for 3D Multi-Object Tracking", In Submission

actiontubes

source code for Finding Action Tubes, CVPR 2015

adda

adnet

Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning (CVPR 2017)

adversarial-feature-augmentation

Code for the paper "Adversarial Feature Augmentation for Unsupervised Domain Adaptation", CVPR 2018

alphapose

Real-Time Multi-Person Pose Estimation System

apollo

An open autonomous driving platform

argoverse-api

Official GitHub repository for Argoverse dataset

async_deep_reinforce

Asynchronous Methods for Deep Reinforcement Learning

awesome-interaction-aware-trajectory-prediction

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

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

awesome-trajectory-prediction

baidu-salers-logo-prediction

baidu contest, url: http://dianshi.baidu.com/gemstone/competitions/detail?raceId=17

benchmark_results

visual tracker benchmark results

caffe-posenet

Implementation of PoseNet

caffe-sal

Amulet and UCF methods for Saliency Detection

caffe_rotate_pool

Rotate RoI Align and Rotate Position Sensitive RoI Align Operation in Caffe

caffeassistant

This is an assistant repository of Caffe to help you easily use Caffe, an extremely efficient deep learning framework.

cf2

Hierarchical Convolutional Features for Visual Tracking (ICCV 2015)

cfnet

(CVPR'17) Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to achieve state-of-the-art performance in tracking, at high-speed.

cfwcr

5th visual object tracking(VOT) tracker CFWCR

cnn-slam

The combination of CNN and SLAM, aiming to achieve a better result of traditional geometric-based SLAM architecture.

cntk

Computational Network Toolkit (CNTK)