Beginner's Guide to Object Detection Algorithms
@author: Akash Kumar
https://www.linkedin.com/in/akash-kumar-9b87b5148/
Object Detection is used almost everywhere these days. The use cases are endless, be it Tracking objects, Video surveillance, Pedestrian detection, Anomaly detection, People Counting, Self-driving cars or Face detection, the list goes on.
ImageAI provided very powerful yet easy to use classes and functions to perform Video Object Detection and Tracking and Video analysis. ImageAI allows you to perform all of these with state-of-the-art deep learning algorithms like RetinaNet, YOLOv3 and TinyYOLOv3. With ImageAI you can run detection tasks and analyse videos and live-video feeds from device cameras and IP cameras.
In my file I have used RetinaNet
What is RetinaNet ?
It is discovered that there is extreme foreground-background class imbalance problem in one-stage detector. And it is believed that this is the central cause which makes the performance of one-stage detectors inferior to two-stage detectors.
In RetinaNet, an one-stage detector, by using focal loss, lower loss is contributed by “easy” negative samples so that the loss is focusing on “hard” samples, which improves the prediction accuracy.
Number of Boxes Comparison in different Algorithm
- YOLOv1: 98 boxes
- YOLOv2: ~1k
- OverFeat: ~1–2k
- SSD: ~8–26k
- RetinaNet: ~100k.
In this model i have used COCO DATASET to train our model
The Common Objects in Context (COCO) dataset has 200,000 images with more than 500,000 object annotations in 80 categories. It is the most extensive publicly available object detection database. The average number of objects is 7.2 per image .
OUTPUT
img:Passenger movie