LEE- Bilgisayar Mühendisliği-Yüksek Lisans

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http://hdl.handle.net/11527/19207

Gözat

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  • Öge
    Learning weights of losses on multiscale in crowd counting
    (Graduate School, 2023) Uysal, Derya ; Bayazıt, Ulug ; 504191508 ; Computer Engineering Programme
    In our daily lives, most of us are in crowded environments, and sometimes we can comment on the crowdedness of the environments we are in. In order to protect the safety of society, crowd analysis is needed to be efficient and executable in highly crowded areas such as shopping malls and stadiums. Cinemas, concerts, touristic places, and popular streets are very important in order to achieve their goals, such as security, entrance-exit information, annual visit rate, and number of instant visitors. Having an idea of the density of public spaces can also be very important in some unexpected situations. For example, in the case of COVID-19, as important as measuring interpersonal distance was, the number of people in a place was also very important. A certain number of people were admitted to public transport, public buildings, etc. Although this is just an example, it shows the importance of crowd analysis in our social lives and the importance of the studies to be done afterwards. There are some studies on performing crowd analysis on visual data. These studies generally have focused on images taken from street cameras or on data collected from the internet. Although some studies target real time, most of the studies in the literature aim to obtain the closest result to the real value of certain image data sets. In this study, like the studies in the literature, it was aimed to reduce the error rate, and proposed methods for it were tried. At the same time, a common data set, including images taken from street cameras and images collected from the internet, was used to make comparisons with studies in the literature. Focusing on the studies using this data set, changes were made in the model architectures. It was desired to achieve more successful results by combining different studies. Since Convolutional Neural Networks (CNN) have been used in this field in recent years and very successful results have been obtained, it was aimed to use a CNN-based architecture in this study. Improvements were made in the optimization part of this study by using the Multiscale Crowd Counting and Localization method, which is a recent approach. It has been shown that the weight parameters used in the architecture can be learned. While the data set used in the literature makes predictions by making a crowd map at the preliminary stage, a point-based approach is followed in this study, and the coordinate information of the people is used. Since the coordinate information is obtained as output, it is determined at the points where the people are. Additionally, some experiments were carried out on dimensions and combining different channels in the model. When the improvements made and the studies in the literature were compared, it was determined that the crowd analysis (number of people) errors on the images were reduced. In addition to the ShanghaiTech data set used in the reference study (Multiscale Crowd Counting and Localization ), the UCF_CC_50 data set was also used and results on both were compared with other studies in the literature. It was observed that the error rate decreased by 12% compared to the reference study.