Pinned Repositories
3DSSD
3DSSD: Point-based 3D Single Stage Object Detector (CVPR 2020)
adaptive-hog-tracker
Object Tracker with adaptive particle filters and HOG detector
adaptive-kalman-filter
Perform optimal state estimation for linear systems with adaptive online noise covariance estimation
adaptive_hog_people_tracker
A ROS package for people tracking for autonomous robots.
Advanced-Lane-Lines
Udacity Self-Driving Car Engineer Nanodegree. Project: Advanced Lane Finding
AirSim
Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
android_ros_pointcloud_viewer
An android application/tool to view pointcloud messages via ros.
AutonomousCar
Autonomous vehicle motion planning implementation/simulation
Autoware
Open-source software for urban autonomous driving
BIHL
The code will be open source after the publication of the paper
JiangChao2009's Repositories
JiangChao2009/Objectness
BING Objectness proposal estimator linux/mac/windows version implementation, runs at 1000 FPS. More in http://mmcheng.net/bing/ and also http://www.robots.ox.ac.uk/~szheng/DepthProposals.html
JiangChao2009/INRIAPersonDataset
INRIA person dataset svm for dlib
JiangChao2009/voc-dpm
Object detection system using deformable part models (DPMs) and latent SVM (voc-release5). You may want to use the latest tarball on my website. The github code may include code changes that have not been tested as thoroughly and will not necessarily reproduce the results on the website.
JiangChao2009/MDP_Tracking
Learning to Track: Online Multi-Object Tracking by Decision Making
JiangChao2009/AirSim
Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
JiangChao2009/Advanced-Lane-Lines
Udacity Self-Driving Car Engineer Nanodegree. Project: Advanced Lane Finding
JiangChao2009/object-proposals
Repository containing wrapper to obtain various object proposals easily
JiangChao2009/vehicle-detection-and-tracking-2
Detecting vehicles in a video stream using machine learning. Adds on to lane detection project.
JiangChao2009/vehicle_detection_hog_svm
Vehicle detection using HOG + SVM and sliding windows
JiangChao2009/Udacity-SDC-Radar-Driver-Micro-Challenge
Udacity Self-Driving Car Radar Driver Micro Challenge
JiangChao2009/WHAT-AI-CAN-DO-FOR-YOU
Breakthrough AI Papers and CODE for Any Industry.
JiangChao2009/transito-cv
Traffic sign detector and classifier that uses dlib and its implementation of the Felzenszwalb's version of the Histogram of Oriented Gradients (HoG) detector
JiangChao2009/phase-congruency-features
C++ implementation of the phase congruency technique for 2D and 3D data
JiangChao2009/Calorie-Estimation-of-Fast-Food
The Calorie Estimation Project can be mainly divided into two parts, identifying food from image, and estimating calorie from certain food image. For the identification of food image, we performed multi-class SVM algorithms, with different features explored and compared, including HOG (Histogram of Gradients), LBP (Linear Binary Pattern) and CNN. The result shows that the local feature LBP performs the best overall. The food calorie data from Internet is collected to conclude a table for easy conversion from food category to calorie.
JiangChao2009/Autoware
Open-source software for urban autonomous driving
JiangChao2009/Robotics-Course-project
Haze can cause poor visibility and loss of contrast in images and videos. In this article, we study the dehazing problem which can improve visibility and thus help in many computer vision applications. An extensive comparison of state of the art single image dehazing methods is done. One simple contrast enhancement method is used for dehazing. Structure- texture decomposition has been used in conjunction with this enhancement method to improve its performance in presence of synthetic noise. Methods which use a haze formation model and attempt at solving an ill-posed problem using computer vision priors are also investigated. The two priors studied are dark channel prior and the non-local prior. Both qualitative and quantitative comparisons for atmospheric and underwater images on all three methods provide a conclusive idea of which dehazing method performs better. All this knowledge has been extended to video dehazing. A video dehazing method which uses the spatial and temporal information in a video is studied in depth. An improved version of video dehazing is proposed in this article, which uses the spatial-temporal information fusion framework but does not suffer from some of its limitations. The new video dehazing method is shown to produce better results on test videos
JiangChao2009/BOP
Boosting Object Proposals: From Pascal to COCO
JiangChao2009/vehicle_detection_haarcascades
Vehicle Detection by Haar Cascades with OpenCV
JiangChao2009/lcm_to_ros
Automatically generate ROS messages and corresponding republishers for LCM messages
JiangChao2009/scratch-detection
JiangChao2009/vsor_clzx
Center of vehicle, institute of application, HeFei institute of physical science, Chinese acadmic of sciences
JiangChao2009/husky_simulator
Simulator packages for the Clearpath Husky
JiangChao2009/novatel_span_driver
Work in progress driver for NovAtel SPAN devices. See: http://wiki.ros.org/novatel_span_driver
JiangChao2009/self-driving
Machine learning models for self-driving cars
JiangChao2009/FHOG
C++ Felzenszwalb HOG extractor
JiangChao2009/tracker_kcf_ros
基于ros下应用深度相机的kcf追踪算法实现
JiangChao2009/decision_making
JiangChao2009/jacktelop
Simple telop example for ROS with LCM -> ROS bridge
JiangChao2009/teleop_tools
A set of generic teleoperation tools for any robot.
JiangChao2009/path_planner
Hybrid A* Path Planner for the KTH Research Concept Vehicle