/Object-Detection-and-Tracking

ObjectDetection Two-stage (RCNN) vs One-stage (YOLO) Detectors and YOLOv3+Deep_SORT/SORT Object Tracking

Primary LanguagePython

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.

[tf-model zoo]

OneStage

YOLO: Real-Time Object Detection

  • How to train a YOLOv3 model on own images - [Link]

  • YOLOv3 + Deep_SORT - Object Counter - [Link]

  • YOLOv3 + SORT - Person Counter - [Link]

  • How to get it working on the COCO dataset coco2voc - [Link]

  • Convert Dataset2Yolo - COCO / VOC -[Link]

SSD: Single Shot MultiBox Detector

  • How to train a SSD model on own images - [Link]

TwoStage

R-CNN: Region-based methods

  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN

Paper list from 2014 to now(2019)

PapersWithCode: Browse > Computer Vision > Object Detection - [Link]

ObjectDetection Two-stage vs One-stage Detectors - [Link]

ObjectDetection mAP & IoU - [Link]

COCO dataset and Pascal VOC dataset - [Link]