Robust human detection with occlusion handling by fusion of thermal and depth images from mobile robot

In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages, pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

Application of Particle Swarm Optimization in Optimizing Stereo Matching Algorithm’s Parameters for Star Fruit Inspection System

This paper reports the finding of the experimentation of the Particle Swarm Optimization in optimizing the stereo matching algorithm’s parameters for the star fruit inspection system. The star fruit inspection system is built by CvviP Universiti Teknologi Malaysia. While the stereo matching algorithm used in the experiment is taken from the Matlab library. Each particle of Particle Swarm Optimization in the search pace repsents a set of candidate numerical value of the stereo matching’s parameters. The fitness function for this application is the sum of absolute error of the gray scale value of both images. Based on this information, the particles will improve its position in the search space by moving towards its best record and the swarm best record. The process repeated until the maximum iteration met. The result indicates that there is potential application of Particle Swarm Optimization in stereo matching’s parameters tuning

An Experimental Study of the Application of Gravitational Search Algorithm in Solving Route Optimization Problem for Holes Drilling Process

Previously, route planning in holes drilling process has been taken for granted due to its automated process, in nature. But as the interest to make Computer Numerical Control machines more efficient, there have been a steady increase in number of studies for the past decade. Many researchers proposed algorithms that belong into Computational Intelligence, due to their simplicity and ability to obtain optimal result. In this study, an optimization algorithm based on Gravitational Search Algorithm is proposed for solving route optimization in holes drilling process. The proposed approach involves modeling and simulation of Gravitational Search Algorithm. The performance of the algorithm is benchmark with one case study that had been frequently used by previous researchers. The result indicates that the proposed approach performs better than most of the literatures

Classification System for Wood Recognition using K-Nearest Neighbor with Optimized Features from Binary Gravitational Algorithm

Woods species recognition is a texture classification difficulty that has been studied by many researchers years ago. The species of the wood are identified by the proposed classification using the textural type that can be observed on the structural features for example the colour of the woods, weight, texture and other features. Any mistakes on texture recognition will affect the economic benefits of wood production where it is an important basis for an identification of woods. Besides, to guide a person to be skilled in wood recognition, it will take a long time and the result the wood sample can be biased. These kinds of problem can be a motivation to develop a system that can recognize the wood effectively. This project will try to integrate both attempts by proposing a classification system consists of feature extractor, classifier and optimizer. The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM’s feature selection and parameters. For this project, images of wood knot from CAIRO UTM database are used for benchmarking the proposed system performance. The result shows that the proposed approach can perform as good as previous literatures with fewer features used as input for the classifier

Improved human detection with occlusion handling from mobile robot

In the field of computer vision, surveillance systems are a type of security devoted to the safety of public and property. One of the task of a surveillance system is human detection. This paper presents a human detection system and development of a robust human detection technique using thermaldepth information in an indoor environment from mobile robot. A novel fusion of thermal-depth information (FTDI) is introduced to improve segmentation process, robust in any lighting condition, and to expedite processing. To deal with occlusion handling a new approach is proposed called Occlusion Human Detector (OCHD) with Pre-detector. This detector is used to classify occluded persons using pixel codes that are established from candidate selection process. The quantitative results show that the proposed system performed well with an average accuracy of over 90% for all datasets, and even outperformed state-of-the-art algorithms. The novelty of the work in this paper has presented that the detection method can be enhance the classification of persons and their occlusion. The advantages of the proposed system are that it is computationally inexpensive and performs well even under severe occlusion and poor illumination

Depth and thermal image fusion for human detection with occlusion handling under poor illumination from mobile robot

In this paper we present a vision-based approach to detect multiple persons with occlusion handling from a mobile robot in real-world scenarios under two lighting conditions, good illumination (lighted) and poor illumination (dark). We use depth and thermal information that are fused for occlusion handling. First, a classifier is trained using thermal images of the human upper-body. This classifier is used to obtain the bounding box coordinates of human. The depth image is later fused with the region of interest obtained from the thermal image. Using the initial bounding box, occlusion handling is performed to determine the final position of human in the image. The proposed method significantly improves human detection even in crowded scene and poor illumination

E-Transform: Development Of An Electronic Educational Quiz Board For Learning High-School's Mathematical Transformation

Mathematical Transformation is a part of Mathematics Secondary grade syllabus in Malaysia where the students being exposed to three topics: translation, reflection and rotation. This paper is an extension of a published proceeding which proposed an educational kit that can be used to study the Topic of Transformation in Mathematics. The educational kit displays a question at an LCD screen in which student need to insert the correct shape at the appropriate slot at the given grid. Then, the educational kit gives feedback to the student. A survey is done to gauge the effectiveness of the educational kit. The result shows that the respondents agreed that the educational kit is suitable for the syllabus and catches the student’s attention. From the same feedback, there is a need to improve the instructional of the educational kit as there are a few respondents among the students do not feel the educational quiz board can be handle without supervisio