Object Detection

 

Image Credit : IEEE Explore

Object detection is the task of localization of objects in an input image.

The definition of an ”object” vary. It can be a single instance or a whole class of objects.

Object detection methods are typically based on the local image features or a sliding window.

The feature-based approaches usually follow the pipeline of:

  (i) feature detection,

 (ii) feature recognition, and

 (iii) model fitting

Exploiting the fact that background is far more frequent than the object, a classifier is separated into a number of stages, each of which enables early rejection of background patches thus reducing the number of stages that have to be evaluated on average. 

Training of such detectors typically requires a large number of training examples and intensive computation in the training stage to accurately represent the decision boundary between the object and background.

An alternative approach is to model the object as a collection of templates. In that case the learning involves just adding one more template .