shenchao8607,ROBUST,github

4yp-roadsigndetection

action-recognition-using-mhi-based-hu-moments-with-hmms

Action recognition from video streams is among the active research topics in computer vision. The challenge is on the identification of the actions robustly regardless of the variations imposed by appearances of actions performed by different people. The challenge increases when the data is gathered from an outdoor environment, i.e. background and illumination variations. This paper proposes a Hidden Markov Model (HMM) based approach to model actions using Hu moments that are computed using a modified Motion History Images (MHI). The experiments performed using Weizmann dataset show that the proposed method generates 99% classification accuracy, which is higher than many state of the art techniques that use MHI and HMMs separately, or at least comparable with them.

als_ros

An advanced localization system for ROS use.

awesome-lidar

😎 Awesome LIDAR list. The list includes LIDAR manufacturers, datasets, point cloud-processing algorithms, point cloud frameworks and simulators.

bluerov2

Scripts to help BlueRov2 integration with ROS and UUV Simulator

breezyslam

Simple, efficient, open-source package for Simultaneous Localization and Mapping in Python, Matlab, Java, and C++

carla

Open-source simulator for autonomous driving research.

carleton-thesis-template-latex

A LaTeX document class that conforms to Carleton University's thesis formatting guidelines.

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

cartographer_ros

Provides ROS integration for Cartographer.

cartographer_turtlebot

Provides TurtleBot integration for Cartographer.

cvg_ardrone2_ibvs

We present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following objects from a multi-rotor UAV, without any dependence on GPS systems. Our proposed method tracks a user specified object continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people, occlusions and also fast moving objects; showing the robustness of the proposed systems against perturbations and illumination changes. Our experiments show that the system is able to track a great variety of objects present in suburban areas, among others: people, windows, AC machines, cars and plants. urls: http://www.vision4uav.eu/?q=following and http://robotics.asu.edu/ardrone2_ibvs/ .