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Top1. Introduction
Advanced concepts like neural networks and deep learning are gaining its ground in the area of computer vision. The solution provided using these techniques can be highly adaptive and reliable in real time. Traditionally, visually challenged people use a white cane for their navigation in outdoors which provides them limited utility. A smart system is required to ensure safety and to make the individual highly aware of his/her surrounding to improve assistance. Before implementing object, detection and classifying the object based on its category, we need to understand the difference between object detection and image classification. Image classification is the process of classifying the image to a category based on the recognized features and patterns, whereas object detection is the process of obtaining the bounding box of coordinates exactly where a particular object is present in the image. We can detect more than one object of a different class in an image. In short, object detection can not only tell us what is in an image but also where the object is as well. There are several ways to detect objects in an image. There are several approaches to detect the objects and it can be broadly classified as a classification problem and regression problem.
1.1. Problem Statement
As we move towards more complete image understanding, having more precise and detailed object recognition becomes crucial. In this context, one cares not only about classifying images, but also about precisely estimating the class and location of objects contained within the images, a problem known as object detection. One of the major problem was that of image classification, which is defined as predicting the class of the image. A slightly complicated problem is that of image localization, where the image contains a single object and the system should predict the class of the location of the object in the image (a bounding box around the object).
Detection involves both classification and localization has the following objectives:
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Object detection, segmentation, location, and recognition.
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Object tracking.
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Different perspectives on the same scene or object-based image retrieval.
1.2. Objectives
The motive of object detection is to recognize and locate all known objects in a scene. Imparting intelligence to machines and making robots more and more autonomous and independent has been a sustaining technological dream for the mankind. It is our dream to let the robots take on tedious, boring, or dangerous work so that we can commit our time to more creative tasks. Unfortunately, the intelligent part seems to be still lagging behind. In real life, to achieve this goal, besides hardware development, we need the software that can enable robot the intelligence to do the work and act independently. One of the crucial components regarding this is vision, apart from other types of intelligences such as learning and cognitive thinking. A robot cannot be too intelligent if it cannot see and adapt to a dynamic environment.
The searching or recognition process in real time scenario is very difficult. So far, no effective solution has been found for this problem. Despite a lot of research in this area, the methods developed so far are not efficient, require long training time, are not suitable for real time application, and are not scalable to large number of classes. Object detection is relatively simpler if the machine is looking for detecting one particular object.
However, recognizing all the objects inherently requires the skill to differentiate one object from the other, though they may be of same type. Such problem is very difficult for machines, if they do not know about the various possibilities of objects.
1.3. Scope
Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them.