In object detection, a bounding box is often represented by the coordinates of its upper-left and lower-right corners. Specifically, the box is determined by the $$x$$ and $$y$$ coordinates of the upper-left corner and the corresponding $$x$$ and $$y$$ coordinates of the lower-right corner within the image.

Two-Corner Bounding Box Representation

In object detection, an alternative bounding box representation uses the $$(x, y)$$-axis coordinates of the bounding box's center point, along with the overall width and height of the box.

Center-Width-Height Bounding Box Representation

When defining locations in an image, such as bounding boxes, a specific coordinate system is used. The origin of the coordinates is located at the upper-left corner of the image. From this origin, moving to the right represents the positive direction of the $$x$$-axis, and moving downward represents the positive direction of the $$y$$-axis.

Claude

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



Object Detection in Computer Vision

Dive into Deep Learning

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 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.

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.

Two Main Groups of Object Detection Algorithms

In object detection, a bounding box is utilized to describe the precise spatial location of a target object within an image. Unlike simple image classification, object detection requires localizing the objects, which is generally achieved by defining a rectangular boundary that perfectly encapsulates the item of interest. These boundaries are plotted on an image coordinate system where the origin $$(0, 0)$$ is situated at the upper-left corner, with the positive $$x$$-axis extending horizontally to the right and the positive $$y$$-axis extending vertically downward.

Learn Before

Related

Learn After