Landmark detection is a fundamental building block of computer vision applications such as face recognition, pose recognition, emotion recognition, head recognition to put the crown on it, and many more in augmented reality.

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In this task, there are multiple objects in an image. The computer should detect objects classes and their locations.



Object Detection in Computer Vision

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Image classification and image localization are recognition of one object in an image.

Object detection is recognition of multi objects in an image.

Single Object Detection vs. Multi-object Detection in Computer Vision


1- Train data as we do for binary image classification.

2- Use sliding window detection.

Object Detection Steps in Computer Vision

Landmark Detection in Computer Vision

There are a few different algorithms for object detection and they can be split into two groups:

1. Algorithms based on classification. They are implemented in two stages. First, they select regions of interest in an image. Second, they classify these regions using convolutional neural networks. This solution can be slow because we have to run predictions for every selected region. A widely known example of this type of algorithm is the Region-based convolutional neural network (RCNN) and its cousins Fast-RCNN, Faster-RCNN and the latest addition to the family: Mask-RCNN. Another example is RetinaNet.

2. Algorithms based on regression – instead of selecting interesting parts of an image, they predict classes and bounding boxes for the whole image in one run of the algorithm. The two best known examples from this group are the YOLO (You Only Look Once) family algorithms and SSD (Single Shot Multibox Detector). They are commonly used for  real-time object detection as, in general, they trade a bit of accuracy for large improvements in speed.

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