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Dilshad Islam
Chittagong Veterinary and Animal Sciences University

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Crowd Detection in Still Images Using Combined HOG and SIFT Feature Machbah Uddin; Hira Lal Gope; Md. Sayeed Iftekhar Yousuf; Dilshad Islam; Mohammad Khairul Islam
Indonesian Journal of Electrical Engineering and Computer Science Vol 4, No 2: November 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v4.i2.pp447-458

Abstract

Person detection and tracking in crowd is a challenging task. We detect the head region and based on this head region we can detect people from crowd. Individual object detection has been improved significantly in recent times but the crowd detection and tracking contains some challenges. Crowd analysis is a highly focused area for law enforcement, urban engineering and traffic management.  There are a lot of incident occurred in crowd area during some fabulous event. In this research low resolution and verities of image orientation is a key factor as well as overlapping person images in crowd misguided the system. An enhanced system of interest point detection based on gradient orientation information as well as improved feature extraction HOG is used for identifying the human head or face from crowd. We have analyzed different types of images in different varieties and found accuracy 88-90%. In a number of applications, such as document analysis and some industrial machine vision tasks, binary images can be used as the input to algorithms that perform useful tasks. These algorithms can handle tasks ranging from very simple counting tasks to much more complex recognition, localization, and inspection tasks. Thus by studying binary image analysis before going on to gray-tone and color images, one can gain insight into the entire image analysis process.
Fire Detection in Still Image Using Color Model Hira Lal Gope; Machbah Uddin; Shohag Barman; Dilshad Islam; Mohammad Khairul Islam
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp618-625

Abstract

Fire incidence is one of the major disasters of human society. This paper proposes a still image-based fire detection system. It has many advantages like lower cost, faster response, and large coverage. The existing methods are not able to detect fire region adequately. The proposed method overcome and addresses the issue. A binary contour image of flame that is capable of classifying fire or no fire in image for fire detection is proposed in this study. The color of fire area can range from red yellow to almost white. So, here it is challenges the detected area is actually fire or no fire. Our propose method consists of five parts. Firstly, the digital image is taken from dataset and the digital image is sampled and mapped as a grid of dots or picture elements. We convert image to separate RGB Color range Matrix. We define some rules to select yellow color range of the image later on converted the image to binary range. Finally, binary contour image of flame information that detect the fire. We have analyzed different types of fire images in different varieties and found accuracy 85-90%.
Co-Authors Hira Lal Gope Machbah Uddin Md. Sayeed Iftekhar Yousuf Mohammad Khairul Islam Shohag Barman