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.
I have tested on Ubuntu 16.04/18.04. The code may work on other systems.
[Ubuntu-Deep-Learning-Environment-Setup]
Clone the repository
git clone https://github.com/yehengchen/Object-Detection-and-Tracking.git
YOLO: Real-Time Object Detection and Tracking
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YOLOv4 + Deep_SORT - Pedestrian Counting - [Link]
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YOLOv3 + Deep_SORT - Pedestrian&Car Counting - [Link]
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YOLOv3 + SORT - Pedestrian Counting - [Link]
Darknet_ROS: Real-Time Object Detection and Rotation Grasp Detection With ROS
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YOLOv3 + ROS Kinetic - For small Custom Data - [Link]
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YOLOv3 + OpenCV + ROS Melodic - Rotation Object Detection - [Link]
SSD: Single Shot MultiBox Detector
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How to train a SSD model on own images - [Link]
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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Mask R-CNN + ROS Kinetic - [Link]
This project is ROS package of Mask R-CNN algorithm for object detection and segmentation.
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COCO dataset and Pascal VOC dataset - [Link]
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How to get it working on the COCO dataset coco2voc - [Link]
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Convert Dataset2Yolo - COCO / VOC - [Link]