TY - JOUR
KW - Video Surveillance
KW - Deep Learning
KW - Convolutional Neural Network (CNN)
KW - Individuals Analysis
KW - Counting Individuals
AU - Anahita Ghazvini
AU - Siti Norul Huda Sheikh Abdullah
AU - Masri Ayob
AB - 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.
IS - Regular Issue
M1 - 5
N2 - 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.
PY - 2019
SP - 7
EP - 14
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - A Recent Trend in Individual Counting Approach Using Deep Network
UR - https://www.ijimai.org/journal/sites/default/files/files/2019/04/ijimai_5_5_1_pdf_11035.pdf
VL - 5
SN - 1989-1660
ER -