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A SYSTEM PROPOSAL FOR COUNTING THE NUMBER OF PEOPLE IN STILL IMAGES

BROWSE_DETAIL_CREATION_DATE: 08-02-2017

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: Technology (General),

BROWSE_DETAIL_CREATORS: Al Zubaidi, Waleed Khalid Mahmood (Author),

BROWSE_DETAIL_CONTRIBUTERS: Koyuncu, Murat (Advisor),

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Counting people in still images, face detection, human body detection, head detection, Viola-Jones algorithm, HOG, crowded images, morphological image operation, circular Hough transform.


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Counting people in images is a challenging task in the computer vison. This thesis targets to estimate the number of people in images accurately. Our focus is on implementing a robust counting algorithm that depends on different approaches together to detect humans having different appearances in images. Three different approaches are taken as the base of the proposed people counting method. These approaches are frontal face detection, human whole body detection, and people head detection.The main contribution of this thesis is using different approaches together for counting people in still images. We have done that by using Viola-Jones algorithm for face detection, the HOG features and SVM classifier for human body detection, and Hough transform and morphological image processing for head detection in crowded images. Any image is processed by three detectors in parallel and detected people are counted. Then, their results are combined for a final decision. The proposed method is implemented in the C++ language with the help of the OpenCV image processing library. The proposed method is tested and compared with some other approaches. The experimental results show that the proposed method achieves more successful results compared to other approaches when test dataset includes various image categories.


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