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metadata.theses.dc.contributor.*: MOHD KHAIRI BIN KAMARUDDIN 28-Nov-2018
metadata.theses.dc.description.abstract: This thesis presented an optimization technique for embedded platform in a collaborative camera for distributed face tracking system. In this work various method,hardware and its configuration for face detection and recognition has been reviewed and implemented. Local Binary Pattern is used as feature extraction method for face detection algorithm. Then Fisherface (Linear Discriminant Analysis) has been implemented for face recognition algorithm. In terms of embedded platform, Raspberry Pi2 has been chosen as a preferred platform to run the system because it is compatible with OpenMP multiprocessing method. The advantage of Raspberry Pi 2 is it has the most number of core and the lowest cost when compare to the other embedded platform which was tested. Several enhancements have been done to the embedded platform which enable multiprocessing for OpenCV libraries using OpenMP and Random Access Memory tuning whereby mounting the log files of the operating system to the cache memory. Every node contains algorithms using Local Binary Pattern as feature extraction method for face detection and Fisherface as recognition technique. Alternate loop has been applied to the system avoiding vision algorithm for every two out of three times. By doing this, the processing time is reduced without compromising the performance of the nodes. This enhancement also further gives a big impact on reducing face detection speed rate. The result shows the optimization of the embedded platform allow the program to increase the number of frame per second from 3 frame per second to 13 frame per second without compromising the number of detection per second which is from 4 to 3 detections. Futher work on the algorithm shows its capability of the collaborative camera to share and centralize the logging information between cameras regarding the moving of samples and gives a warning if suspicious unregistered sample intrude to surveillance area.
metadata.theses.dc.theses.semester: September 2018
metadata.theses.dc.theses.course: Degree of Master in Engineering Technology
Appears in Collections:Master Theses

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