Object Detection and Tracking
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
Environment
I have tested on Ubuntu 16.04/18.04. The code may work on other systems.
[Ubuntu-Deep-Learning-Environment-Setup]
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Ubuntu 16.04 / 18.04
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ROS Kinetic / Melodic
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GTX 1080Ti / RTX 2080Ti
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python 2.7 / 3.6
Installation
Clone the repository
git clone https://github.com/yehengchen/Object-Detection-and-Tracking.git
[OneStage]
YOLO: Real-Time Object Detection and Tracking
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[Link]
YOLOv4 + Deep_SORT - Pedestrian Counting - -
[Link]
YOLOv3 + Deep_SORT - Pedestrian&Car Counting -
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[Link]
YOLOv3 + SORT - Pedestrian Counting -
Darknet_ROS: Real-Time Object Detection and Rotation Grasp Detection With ROS
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[Link]
YOLOv3 + ROS Kinetic - For small Custom Data -
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[Link]
YOLOv3 + OpenCV + ROS Melodic - Rotation Object Detection -
SSD: Single Shot MultiBox Detector
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[Link]
How to train a SSD model on own images -
[TwoStage]
R-CNN: Region-based methods
Fast R-CNN / Faster R-CNN / Mask R-CNN
How to train a Mask R-CNN model on own images - [Link]
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[Link]
Mask R-CNN + ROS Kinetic -
This project is ROS package of Mask R-CNN algorithm for object detection and segmentation.
COCO & VOC Datasets
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[Link]
COCO dataset and Pascal VOC dataset - -
[Link]
How to get it working on the COCO dataset coco2voc - -
[Link]
Convert Dataset2Yolo - COCO / VOC -