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Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/476
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dc.contributor.authorMERABET, MUstapha ZAkaria-
dc.contributor.authorDJAMAI, ABdallah ALaa EDdine-
dc.date.accessioned2023-10-15T07:55:43Z-
dc.date.available2023-10-15T07:55:43Z-
dc.date.issued2023-
dc.identifier.urihttps://repository.esi-sba.dz/jspui/handle/123456789/476-
dc.descriptionEncadreur : Dr. KHALDI Belkacemen_US
dc.description.abstractABSTRACT : 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.isoenen_US
dc.subjectCrowd Countingen_US
dc.subjectCNNSen_US
dc.subjectDensity Estimationen_US
dc.subjectEvaluation Metricsen_US
dc.subjectIoss Functionsen_US
dc.subjectTansformersen_US
dc.titleReal-Time On-Board Counting System in Crowded Scenesen_US
dc.typeThesisen_US
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