期刊名称：International Journal of Interactive Multimedia and Artificial Intelligence
印刷版ISSN：1989-1660
出版年度：2019
卷号：5
期号：5
页码：7-4
DOI：10.9781/ijimai.2019.04.003
语种：English
出版社：ImaI-Software
摘要：In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results.
