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-SORT: Real-Time Object Detection and Tracking
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YOLOv4 + Deep_SORT - Pedestrian Counting & Social Distance - [Here]
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YOLOv3 + Deep_SORT - Pedestrian&Car Counting - [Here]
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YOLOv3 + SORT - Pedestrian Counting - [Here]
Darknet_ROS: Real-Time Object Detection and Grasp Detection
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YOLOv3 + ROS Kinetic - For small Custom Data - [Here]
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YOLOv3 + OpenCV + ROS Melodic - Object Detection (Rotated) - [Here]
DeepLabv3+_ROS: Mars Rover - Real-Time Object Tracking
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DeepLab + OpenCV + ROS Melodic/Gazebo - Object Tracking - [Here]
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Mars_Rover + ROS Melodic/Gazebo - [Here]
SSD: Single Shot MultiBox Detector
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 - [Here]
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Mask R-CNN + ROS Kinetic - [Here]
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 - [Here]
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How to get it working on the COCO dataset coco2voc - [Here]
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Convert Dataset2Yolo - COCO / VOC - [Here]
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