DC Field | Value | Language |
dc.contributor.author | MERABET, MUstapha ZAkaria | - |
dc.contributor.author | DJAMAI, ABdallah ALaa EDdine | - |
dc.date.accessioned | 2023-10-15T07:55:43Z | - |
dc.date.available | 2023-10-15T07:55:43Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | https://repository.esi-sba.dz/jspui/handle/123456789/476 | - |
dc.description | Encadreur : Dr. KHALDI Belkacem | en_US |
dc.description.abstract | ABSTRACT :
Crowd counting is a useful tool for situational awareness in public spaces. Automated
crowd counting with videos and images is an interesting but difficult task that
has attracted a lot of interest in computer vision. Different deep learning techniques
have been developed recently to reach cutting-edge performance. Numerous features
of the techniques that have been evolved throughout time cover model architecture,
learning paradigm, computing complexity, input pipeline, accuracy gains, etc.
Many researchers are devoting to crowd counting, and many excellent works of literature
and works have spurted out. These works usually aim to be be helpful for the
development of crowd counting. However, the question we should consider is why and
how they are effective for this task. In this paper, we have surveyed many works to
comprehensively and systematically study the crowd counting models, mainly CNNbased
density map estimation methods.
This thesis is aimed to categorize, analyze as well as provide the latest development
and performance evolution in crowd counting using different deep learning techniques
and methods that are published in journals and conferences over the past five years. | en_US |
dc.language.iso | en | en_US |
dc.subject | Crowd Counting | en_US |
dc.subject | CNNS | en_US |
dc.subject | Density Estimation | en_US |
dc.subject | Evaluation Metrics | en_US |
dc.subject | Ioss Functions | en_US |
dc.subject | Tansformers | en_US |
dc.title | Real-Time On-Board Counting System in Crowded Scenes | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Master
|