Analysis of Unclean Hand System Detection Using Template Matching Technique

The aim of this project is to audit the handwashing technique of hospital staff that may cause infection to the patients. This project is to detect unclean washed hands using image processing technique specifically template matching. The detection and recognition of palm in images is the key methodology of this paper. The prototype used for capturing hand images is a dark box with UV light and a camera. Target will need to apply Glogerm on their hands that imitate bacteria. Hence, when they wash their hands inappropriately, Glogerm can be seen in the captured images under the UV light as the unwanted stain on washed hands, the target handwashing technique needs to be improved. Templates of the missed area of washed hands are used to compare the correctness of hand washed techniques by the target. Data of 100 images were taken, results are; 100% accuracy of the hand image without Glogerm, 56.67% of the image that did not wash using water after applying the Glogerm and 45.45% accurate when user wash their hand by using water after applying Glogerm. The overall efficiency of the system in detecting the missed part is 51% accuracy As a summary, this project accurately detects stain percentage that represents the missed part when applying the template matching technique

Penghitungan Orang dengan Metode Gaussian Mixture Model dan Human Presence Map Studi Kasus: Penghitungan Orang di dalam Kelas

Penggunaan teknologi dewasa ini telah banyak digunakan dalam kehidupan sehari-hari yang mungkin kehadirannya bahkan tidak terasa oleh masyarakat. Salah satunya adalah penggunaan CCTV yang dapat berfungsi sebagai suatu perangkat pengawasan. Namun disamping fungsi utamanya tersebut, CCTV sebagai kepanjangan dari mata orang bisa digunakan untuk melakukan penghitungan orang. Penulis dalam Tugas Akhir ini membahas tentang penghitungan orang dengan studi kasus spesifik yakni menghitung orang dalam suatu kelas. Manfaat yang bisa diambil dalam mengambil estimasi jumlah orang dalam suatu kelas antara lain untuk mengecek apakah kehadiran sesuai dengan jumlah orang dalam kelas. Penghitungan orang dilakukan dengan menggunakan background substraction yakni menggunakan metode Gaussian Mixture Model (GMM) untuk melakukan ekstraksi antara objek yang ingin diamati, dalam hal ini orang dengan background. Selanjutnya proses klasifikasi melalui Human Presence Map untuk memfokuskan pendeteksian piksel dimana orang berada sehingga dapat dilakukan penghitungan orang berdasarkan suatu area wilayah yang telah ditentukan. Dengan metode tersebut, penghitungan orang dapat mencapai tingkat akurasi sebesar 91% untuk kasus deteksi satu orang dalam video uji duduk terpisah. Kata Kunci: people counting, ruang kelas, gaussian mixture model, human presence map, pikse

A People Counter Using Top-view Video Sequences

[[abstract]]In this thesis, we propose a bi-directional people counting system based on top-view video sequences. The system is divided into two sub-systems: people detection and people tracking. For people detection, we extract foreground object by two features of object: attention region and the boundary between objects and background. The attention region is generated by motion detection. Since people may stay at same location, our system should consider the stopping objects into attention region. Then we can use level set method to extract the objects. For people counting, we use particle filter to tracking objects to solve object merge-split problem. Each particle is represented by an ellipse. One object is tracking by a set of particles. The likelihood function of the particle weight is defined by location, color, and shape style. Using expect state to be output of our system. Then we use the tracking result to count number of people. Our system has been tested in different lighting conditions (e.g. weather, time, and environment) and using video sequences catching from different camera types (e.g. ordinary, and fish eye cameras) to show the robust of system.