Object Detection using Single Shot MultiBox Detector with Caffe MobileNet on OpenCV in Python.
SSD Framework
Single Shot MultiBox Detectors can be divided into two parts:
Extracting Features using a base network
Using Convolution Filters to make predictions
This implementation makes use of the MobileNet deep learning CNN architecture as the base network.
What is Object Detection?
Object Detection in Computer Vision is as simple as it sounds- detecting and predicting objects and localizing their area. Object Detection is based on image classification. Irrespective of the latter being performed using neural networks or primitive classifiers, image classification is always the first step. Building further on this, we can perform detection which localizes all possible objects in a given frame.
Single Shot MultiBox Detector (SSD)
SSD Object Detection extracts feature map using a base deep learning network, which are CNN based classifiers, and applies convolution filters to finally detect objects. Our implementation uses MobileNet as the base network (others might include- VGGNet, ResNet, DenseNet).
What is Caffe? Caffe is a deep learning framework developed by Berkeley AI Research and community contributors. Caffe was developed as a faster and far more efficient alternative to other frameworks to perform object detection. Caffe can process 60 million images per day with a single NVIDIA K-40 GPU. That is 1 ms/image for inference and 4 ms/image for learning.
Code Implementation Requirements
Python (ver 3.6) and OpenCV (ver 4.2)
Caffe MobileNet SSD model weights and prototxt definition..
The demo video :
To see demo : https://youtu.be/8mkcah6QDG4