All Projects → appinho → Sarosperceptionkitti

appinho / Sarosperceptionkitti

Licence: mit
ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark Suite

Programming Languages

python
139335 projects - #7 most used programming language
cpp
1120 projects

Projects that are alternatives of or similar to Sarosperceptionkitti

Mask rcnn ros
The ROS Package of Mask R-CNN for Object Detection and Segmentation
Stars: ✭ 53 (-72.54%)
Mutual labels:  object-detection, ros, semantic-segmentation
Self Driving Golf Cart
Be Driven 🚘
Stars: ✭ 147 (-23.83%)
Mutual labels:  object-detection, ros, semantic-segmentation
Jacinto Ai Devkit
Training & Quantization of embedded friendly Deep Learning / Machine Learning / Computer Vision models
Stars: ✭ 49 (-74.61%)
Mutual labels:  object-detection, semantic-segmentation
Darknet ros
YOLO ROS: Real-Time Object Detection for ROS
Stars: ✭ 1,101 (+470.47%)
Mutual labels:  object-detection, ros
Raster Vision
An open source framework for deep learning on satellite and aerial imagery.
Stars: ✭ 1,248 (+546.63%)
Mutual labels:  object-detection, semantic-segmentation
Ros yolo as template matching
Run 3 scripts to (1) Synthesize images (by putting few template images onto backgrounds), (2) Train YOLOv3, and (3) Detect objects for: one image, images, video, webcam, or ROS topic.
Stars: ✭ 32 (-83.42%)
Mutual labels:  object-detection, ros
Awesome Semantic Segmentation
🤘 awesome-semantic-segmentation
Stars: ✭ 8,831 (+4475.65%)
Mutual labels:  semantic-segmentation, evaluation
Vidvrd Helper
To keep updates with VRU Grand Challenge, please use https://github.com/NExTplusplus/VidVRD-helper
Stars: ✭ 81 (-58.03%)
Mutual labels:  object-detection, evaluation
Gluon Cv
Gluon CV Toolkit
Stars: ✭ 5,001 (+2491.19%)
Mutual labels:  object-detection, semantic-segmentation
Pytorch cpp
Deep Learning sample programs using PyTorch in C++
Stars: ✭ 114 (-40.93%)
Mutual labels:  object-detection, semantic-segmentation
Evo
Python package for the evaluation of odometry and SLAM
Stars: ✭ 1,373 (+611.4%)
Mutual labels:  ros, evaluation
Pick Place Robot
Object picking and stowing with a 6-DOF KUKA Robot using ROS
Stars: ✭ 126 (-34.72%)
Mutual labels:  object-detection, ros
Dodo detector ros
Object detection from images/point cloud using ROS
Stars: ✭ 31 (-83.94%)
Mutual labels:  object-detection, ros
Medicaldetectiontoolkit
The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
Stars: ✭ 917 (+375.13%)
Mutual labels:  object-detection, semantic-segmentation
Efficientdet Pytorch
A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
Stars: ✭ 906 (+369.43%)
Mutual labels:  object-detection, semantic-segmentation
Autonomous driving
Ros package for basic autonomous lane tracking and object detection
Stars: ✭ 67 (-65.28%)
Mutual labels:  object-detection, ros
Involution
[CVPR 2021] Involution: Inverting the Inherence of Convolution for Visual Recognition, a brand new neural operator
Stars: ✭ 252 (+30.57%)
Mutual labels:  object-detection, semantic-segmentation
Edgenets
This repository contains the source code of our work on designing efficient CNNs for computer vision
Stars: ✭ 331 (+71.5%)
Mutual labels:  object-detection, semantic-segmentation
Frostnet
FrostNet: Towards Quantization-Aware Network Architecture Search
Stars: ✭ 85 (-55.96%)
Mutual labels:  object-detection, semantic-segmentation
Paz
Hierarchical perception library in Python for pose estimation, object detection, instance segmentation, keypoint estimation, face recognition, etc.
Stars: ✭ 131 (-32.12%)
Mutual labels:  object-detection, semantic-segmentation

License: MIT

SARosPerceptionKitti

ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark

Demo

Setup

Sticking to this folder structure is highly recommended:

    ~                                        # Home directory
    ├── catkin_ws                            # Catkin workspace
    │   ├── src                              # Source folder
    │       └── SARosPerceptionKitti         # Repo
    ├── kitti_data                           # Dataset
    │   ├── 0012                             # Demo scenario 0012
    │   │   └── synchronized_data.bag        # Synchronized ROSbag file
  1. Install ROS and create a catkin workspace in your home directory:
mkdir -p ~/catkin_ws/src
  1. Clone this repository into the catkin workspace's source folder (src) and build it:
cd ~/catkin_ws/src
git clone https://github.com/appinho/SARosPerceptionKitti.git
cd ~/catkin_ws
catkin_make
source devel/setup.bash
  1. Download a preprocessed scenario and unzip it into a separate kitti_data directory, also stored under your home directory:
mkdir ~/kitti_data && cd ~/kitti_data/
mv ~/Downloads/0012.zip .
unzip 0012.zip
rm 0012.zip

Usage

  1. Launch one of the following ROS nodes to perform and visualize the pipeline (Sensor Processing -> Object Detection -> Object Tracking) step-by-step:
source devel/setup.bash
roslaunch sensor_processing sensor_processing.launch home_dir:=/home/YOUR_USERNAME
roslaunch detection detection.launch home_dir:=/home/YOUR_USERNAME
roslaunch tracking tracking.launch home_dir:=/home/YOUR_USERNAME
  • Default parameters:
    • scenario:=0012
    • speed:=0.2
    • delay:=3

Without assigning any of the abovementioned parameters the demo scenario 0012 is replayed at 20% of its speed with a 3 second delay so RViz has enough time to boot up.

  1. Write the results to file and evaluate them:
roslaunch evaluation evaluation.launch home_dir:=/home/YOUR_USERNAME
cd ~/catkin_ws/src/SARosPerceptionKitti/benchmark/python
python evaluate_tracking.py

Results for demo scenario 0012

Class MOTA MOTP MOTAL MODA MODP
Car 0.881119 0.633595 0.881119 0.881119 0.642273
Pedestrian 0.546875 0.677919 0.546875 0.546875 0.836921

Contact

If you have any questions, things you would love to add or ideas how to actualize the points in the Area of Improvements, send me an email at [email protected] ! More than interested to collaborate and hear any kind of feedback.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].