A review and an approach for object detection in images. (2017)

• Record Type: Journal Article
• Authors: Sharma, Kartik Umesh; Thakur, Nileshsingh V.
• Is Part Of: International journal of computational vision and robotics. Volume 7:Number 1/2(2017)
• Page Start: 196
• Page End: 237
• Publication Date: 2017
• Subjects: object detection -- localisation -- categorisation -- object recognition -- digital images -- videos -- feature extraction; Computer vision -- Periodicals; Robotics -- Periodicals; Artificial intelligence -- Periodicals; 006.3705
• Languages: English
• ISSNs: 1752-9131

Abstract

An object detection system finds objects of the real world present either in a digital image or a video, where the object can belong to any class of objects namely humans, cars, etc. In order to detect an object in an image or a video the system needs to have a few components in order to complete the task of detecting an object, they are a model database, a feature detector, a hypothesiser and a hypothesiser verifier. This paper presents a review of the various techniques that are used to detect an object, localise an object, categorise an object, extract features, appearance information, and many more, in images and videos. The comments are drawn based on the studied literature and key issues are also identified relevant to the object detection. Information about the source codes and online datasets is provided to facilitate the new researcher in object detection area. An idea about the possible solution for the multi class object detection is also presented. This paper is suitable for the researchers who are the beginners in this domain.