Browsing by Author "Chutiwan Boonarchatong"

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    Human Tracking System from Video Based on Blob Analysis and Object Segmentation Method
    (Institute of Electrical and Electronics Engineers Inc., 2018) Chutiwan Boonarchatong; Mahasak Ketcham
    The research aims to propose a method of video human tracking system. The methodology algorithms are compound with motion vector, optical flow, object segmentation, and Blob analysis. An experiment is set on two situations, which are difference places and difference distance between camera and backgrounds. The results display that the percentage of accuracy has 77.76% in difference 7 areas but the similar distance between camera and human. The other experiment shows that the best distance between camera and human is around 4-30 meters. Therefore, the system would work well when the camera set on the proper area and distance, between camera and human. Thus, the system would contribute to human behavior to suspend the crime with real-Time operating. � 2018 IEEE.
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    IoT Smart Innovation Bin for Promote Learning Garbage Segregation
    (2023-12-19) Chutiwan Boonarchatong; Thinnagorn Chunhapataragul; Saisuda Pantrakool; Phuripoj Kaewyong; Kanitta Wongma; Kongsak Boonarchatong
    The aims of this research were to develop smart innovative trash bins to be used as learning media for garbage segregation and to assessment the learning outcomes after playing the game with smart innovative trash bins. The sample group consisted of 400 grade 4, 5, and 6 students. A set of smart innovative trash bin contains four trash bins classified by type: 1) biodegradable garbage (green bin), 2) hazardous garbage (red bin), 3) general garbage (blue bin), and 4) recycle garbage (yellow bin). The bin embedded the program code in the Arduino board. Twelve garbage items, four types, had RFID tags. When users bring garbage into the correct type of garbage bin, the trash bin lid will open and close by itself. On the other hand, if the garbage is placed in the wrong garbage bin, the lid will not open. The result shown the sample group’s learning outcome of garbage segregation increased by with 3.52 scores or 17.6%. In summary, smart innovative trash bins able to promote learning outcomes of garbage segregation.
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    Performance analysis of edge detection algorithms with THEOS satellite images
    (Institute of Electrical and Electronics Engineers Inc., 2017) Chutiwan Boonarchatong; Mahasak Ketcham
    The goal of this research is to find a suitable edge detection algorithm with 4 bands, B1, B2, B3, and B4 of different types of satellite image. In this paper, the dataset is derived from raw satellite images, namely THEOS, of the seashore in the Samut Prakan province, a fruit garden in the Chantaburi province and river line in Ayuthaya provinces. Edge detection performance algorithms are Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and edge detection processing time, as well as qualitative human visual perception. Our result shows that Canny algorithm and Laplacian of Gaussian algorithm were the best edge detection algorithm for both qualitatively and quantitatively. Moreover, the different kinds of bands in satellite images illustrated the same result. All of the algorithms consumed similar times. � 2017 IEEE.