Object Detection

Object Detection is a fundamental computer vision task that involves identifying and localizing objects or specific features within an image or a video stream. It plays a crucial role in numerous applications, including autonomous driving, surveillance, robotics, and image analysis. Object detection goes beyond mere object recognition by providing both the classification of objects into predefined categories or classes and the precise localization of where these objects exist within the image.

Sliding Window Algorithm

The sliding Window Algorithm is one of the important steps in Object detection algorithms. In the sliding window algorithm, we select a path or a region of the image by sliding a window or box over it and then classify each region that the window covers using an object recognition model. Over the course of the image, every object is thoroughly searched for. We must not only look at every available spot on the image but also at various scales. This is the case because many times object recognition models are trained on a certain set of scales. As a result, classification is required for many image regions or patches.

But the issue still persists. For objects with a fixed ratio, such as faces or people, the sliding window algorithm works well. According to the angle, the features of the objects can change dramatically. But when we search for different aspect ratios, this approach is a little more expensive.

Region Proposal Algorithm

The problem in the sliding window algorithm can be addressed using the region proposal algorithm. With the use of this technique, areas of a picture that are likely to contain objects are encapsulated by the bounding boxes. These region proposals typically contain proposals that nearly resemble the real object’s position, even though they occasionally may be noisy, overlapping or improperly aligned with those boundaries. The classification of these recommendations can then be done using object recognition models. The location of perspective objects is detected in areas with high probability.

In order to do this, the region proposal algorithm uses segmentation, which groups neighbouring image regions with comparable properties. The sliding window strategy, in contrast, looks for objects at all pixels places and sizes. Instead, these algorithms divide images into fewer regions, producing a much lower number of categorization suggested options. To allow for possible differences in object size and shape, these generated ideas are available in a range of scales and aspect ratios.

Region proposal method aims for high recall, prioritizing inclusion of regions with objects. This may result in number of proposals, including the false positives. While this can increase processing time and slight affect accuracy, its crucial to avoid missing actual objects, making high recall a valuable priority in object detection.

OpenCV is a Python library that is used to study images and video streams. It basically extracts the pixels from the images and videos (stream of image) so as to study the objects and thus obtain what they contain. It contains low-level image processing and high-level algorithms for object detection, feature matching etc.

In this article, we will dive into a computer vision technique i.e. selective search for Object Detection in OpenCV.