01961nas a2200241   4500000000100000000000100001008004100002260001200043653002300055653001800078653003900096653002500135653002500160100002100185700003600206700001500242245007000257856009600327300000900423490000600432520126700438022001401705       2019                            d  c06/201910aVideo Surveillance10aDeep Learning10aConvolutional Neural Network (CNN)10aIndividuals Analysis10aCounting Individuals1 aAnahita Ghazvini1 aSiti Norul Huda Sheikh Abdullah1 aMasri Ayob00aA Recent Trend in Individual Counting Approach Using Deep Network  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_1_pdf_11035.pdf  a7-140 v53 aIn 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.  a1989-1660