A survey of detecting pedestrians from low resolution imagery. (2017)

• Record Type: Journal Article
• Authors: Htike, Kyaw Kyaw; Chin, Neoh Siew; Htike, Zaw Zaw; Onn, Choo Wou
• Is Part Of: International journal of intelligent systems design and computing. Volume 1:Number 3/4(2017)
• Page Start: 231
• Page End: 261
• Publication Date: 2017
• Subjects: low resolution -- detecting pedestrians -- object detection -- image analytics -- computer vision; Artificial intelligence -- Periodicals; Human-computer interaction -- Periodicals; 006.3
• Languages: English
• ISSNs: 2052-8477

Abstract

Being able to detect pedestrians in image or video has numerous potential benefits in many diverse applications such as image retrieval, elderly monitoring and safety, person counting and driver assistance systems. Although much work have been done for pedestrian detection, recent state-of-the-art research indicate that a lot of improvements still need to be made, especially when it comes to low resolution imagery. Despite a number of review papers on pedestrian detection that have been published, there is a great need for a survey paper that focuses on pedestrian detection for low resolution data. In this paper, we perform an in-depth critical analysis and review of the most representative and relevant papers in this area, including identification and breaking down of the pipeline for low resolution pedestrian detection systems, as well as, discussing and analysing the underlying causes behind low resolution data as well as recommending potential solutions.