Different Methods Used For Object Detection

Object detection in the context of image processing and AI is defined
as the process of identifying or recognizing an object or item of interest
in a scene or image, along with its location in the scene or image.
For videos, object detection is generally combined with an object
tracking algorithm to ensure the continuity of the identified objects
across multiple scenes. The choice of object detection algorithm
varies based on the objective of the use case.

Based on this, the type of image annotation techniques also varies from common box type or polygonal annotation to segmentation masks and superpixel annotations.

The methods used for object detection are the following:

• Boosted cascade classifiers: Also called Coarse-to-Fine classifiers, they work by defining a set of filters to evaluate image segments. Then, they eliminate image segments that do not match a predefined object. The efficiency achieved with this approach is high.
• Dictionary-based object detection algorithms: These algorithms check for the presence or absence of a specific object in an image, giving them higher priority over other unknown objects in the background. They can also be used to detect the co-occurrence of more than one class of objects in an image.
• Partial object handling: In this approach, every object is described as a set of parts that can be measured. The descriptors of these parts may use oriented gradients. Partial object handling, when used along with the boosted cascade classifiers, speeds up the object detection process.
• Convolutional neural network (CNN): This is a widely used object detection method these days. This algorithm allows the model to learn the objects’ features and classify the objects based on the values of the features present rather than following a rule-based programmatic approach for object detection or classification.
• Structured algorithms: Each module is dedicated to a different kind of detected item: module for objects, module for features, module for text, and so on. Each of the module’s parameters are set by training.

As you know, Object detection aims to identify the type of objects inside an image with their relative location. For any object detection task, the object is defined by the user. For example, if the camera in front of a car is required to detect all cars and people in the vicinity, the ‘objects of interest’ will be cars and people. First, the images of these objects need to be collected. Then, a suitable algorithm for object detection needs to be identified.

Hope this was helpful.