Autonomous navigation of mobile robot using kinect sensor

The problem of achieving real time process in depth camera application, in particular when used for indoor mobile robot localization and navigation is far from being solved. Thus, this paper presents autonomous navigation of the mobile robot by using Kinect sensor. By using Microsoft Kinect XBOX 360 as the main sensor, the robot is expected to navigate and avoid obstacles safely. By using depth data, 3D point clouds, filtering and clustering process, the Kinect sensor is expected to be able to differentiate the obstacles and the path in order to navigate safely. Therefore, this research requirement to propose a creation of low-cost autonomous mobile robot that can be navigated safely

Analysis of artificial neural network and viola-jones algorithm based moving object detection

In recent years, the worrying rate of street crime has demanded more reliable and efficient public surveillance system. Analysis of moving object detection methods is presented in this paper, includes Artificial Neural Network (ANN) and Viola-Jones algorithm. Both methods are compared based on their precision of correctly classify the moving objects. The emphasis is on two major issues involve in the analysis of moving object detection, and object classification to two groups, pedestrian and motorcycle. Experiments are conducted to quantitatively evaluate the performance of the algorithms by using two types of dataset, which are different in term of complexity of the background. The utilization of cascade architecture to the extracted features, benefits the algorithm. The algorithms have been tested on simulated events, and the more suitable algorithm with high detection rate is expected to be presented in this paper

Analysis on background subtraction for street surveillance

Background subtraction is a well-known technique used in computer vision applications. However, in public surveillance system, the utilization of background subtraction still new and far from being solved. Insufficient analysis of the background subtraction algorithms made the situation getting worse. The analysis of the commonly-used algorithms is presented in this paper. Experiments are conducted to quantitatively evaluate the performance of the algorithms by using three video sequences. The more suitable algorithm for various conditions is expected to be presented as the results in this paper

Detection of different classes moving object in public surveillance using artificial neural network (ANN)

Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance Systems. Street crimes such as snatch theft is increasing drastically in recent years, cause a serious threat to human life worldwide. In this paper, a moving object detection and classification model was developed using novel Artificial Neural Network (ANN) simulation with the aim to identify its suitability for different classes of moving objects, particularly in public surveillance conditions. The result demonstrated that the proposed method consistently performs well with different classes of moving objects such as, motorcyclist, and pedestrian. Thus, it is reliable to detect different classes of moving object in public surveillance camera. It is also computationally fast and applicable for detecting moving objects in real-time

Moving object detection and classification using neuro-fuzzy approach

Public surveillance monitoring is rapidly finding its way into Intelligent Surveillance System. Street crime is increasing in recent years, which has demanded more reliable and intelligent public surveillance system. In this paper, the ability and the accuracy of an Adaptive Neuro-Fuzzy Inference System (ANFIS) was investigated for the classification of moving objects for street scene applications. The goal of this paper is to classify the moving objects prior to its communal attributes that emphasize on three major processes which are object detection, discriminative feature extraction, and classification of the target. The intended surveillance application would focus on street scene, therefore the target classes of interest are pedestrian, motorcyclist, and car. The adaptive network based on Neuro-fuzzy was independently developed for three output parameters, each of which constitute of three inputs and 27 Sugeno-rules. Extensive experimentation on significant features has been performed and the evaluation performance analysis has been quantitatively conducted on three street scene dataset, which differ in terms of background complexity. Experimental results over a public dataset and our own dataset demonstrate that the proposed technique achieves the performance of 93.1% correct classification for street scene with moving objects, with compared to the solely approaches of neural network or fuzzy

Thermal degradation study of waste polymer matrix polypropylene and polyvinyl chloride as polymer matrix filled rice husk decking structure

The aim of this study is to find another alternative material to replace the additives of polymeric materials. From previous works, wood-chips are used to act as filler instead of rice husk. Thus, in this case, the woodchips is being exchange with rice husk and mixed with the waste polymer in order to reduce the cost. This study is about the effect of thermal towards the mechanical and physical properties of this WPC. Therefore, heat test and UV Irradiation Exposure test were conducted on this wood polymer composite (WPC). Then, three pa-rameters were being examined which were difference in density, hardness and physical properties after undergoing heat test and UV Irradiation Exposure test. The density and hardness appearances from both test only had slightly reduction and it can be considered as negligible. The physical changes were not too apparent such as crack that would lead to failure even after being heated and exposed to the UV light for a long period. Thus, it proves that rice husk could replace woodchips in order to be used as decking structure

Mechanical properties of wood polymer composites (WPCs) after prolonged ultra-violet (UV) irradiation exposure

This research aims to compare the mechanical properties of WPC made from 30 % of rice husk as fibre and 70 % of polypropylene as polymer with and without prolonged UV irradiation exposure. WPC samples are tested with UV irradiation at 5000, 10000, 15000 and 20000 hours with temperature 50 °C and tested with compression test (ASTM D695-15) and tensile test (ASTM D638-02), while physi-cal properties characterization was done by density test (ASTM D792-13) and OM test. The compression and tensile result shows de-crease value of mechanical properties due to the presence of chain breaking activity contributed from the process of photo oxidation throughout the UV exposure. The effect of UV exposure also resulted in decrease of WPC samples density value as the longer the expo-sure, the lower the samples density due to the losses of volatile particle. In conclusion, the weather ability of WPC was observed by pro-longed exposure to UV radiation thus highlights the influence of parameters involved in the degradation rate of WPC as to contribute a good guidelines and resolution for the future growth of polymer composite structure

Influence of pavement condition towards accident number on Malaysian highway

Accidents in Malaysia are alarming and have been in the rise for the past decades. Increasing numbers of road users, condition of road surface, expanded road network and weather conditions are being carefully looked into determining the contributors towards fatality. Human factors has been widely known and established as one of the main contributor to road accidents. Therefore, this study had focused more on to pavement conditions, where its relation with accidents needs to be further explored. The purpose of this study is to determine the influence of pavement condition towards accident number on Malaysian Highway. Pavement condition data which consist of Mean Texture Depth (MTD), Rutting Depth (RD) and also International Roughness Index (IRI) were measured along 265 km southern stretch of Malaysia highway, by using Multi Laser Profiler (MLP). Accident data were also collected from the accident report prepared by Southern region of Malaysian Highway Authority. Based on the results, it was found that more than 70% of the accidents happened at a good condition of road which having high MTD, low RD and minimum IRI. MTD was found as the most significance parameters that relates to accident. Other factors such as human negligence and environments (weather and lighting), might be the major cause that contribute to the accidents that happened along Malaysian highway

Features selection for multi-camera tracking

Snatch theft is becoming more prevalent in Malaysia nowadays and proper measures must be taken to reduce it. CCTV surveillance systems have been widely used as a street crime prevention tool across public and private areas. Tracking the same object within different cameras' view is essential in many surveillance applications. Recently, most of the researchers have grown more interest on how to track objects across cameras due to the increasing number of cameras. However, the current approach proposed by the researchers still offer trade-off in terms of its accuracy and speed. As the tracking accuracy increases, the speed will decrease that acts reversely proportional to it. This paper presents a novel approach to track moving objects across distributed cameras that provides the most optimal trade-off based on color, texture and edge features. The color, edge and texture features for target and candidate blobs are computed by a novel computational algorithm. This study focuses on analyzing of video surveillance in public places, specifically in outdoor environment. In the result section, the comparison between the effectiveness of the features used in the tracking algorithm is presented. The performance of the method is analyzed based on its accuracy and speed. The more suitable features are identified. Experimental results show the effectiveness of this method for real-time operation

Real-time tracking using edge and color feature

Visual surveillance system is an important tool used for monitoring a scene in order to detect any suspicious behavior. Recently, due to the increasing number of cameras, most of the researchers have shown more interest on how to track objects across the cameras. However, the existing algorithms that have been proposed by earlier researchers still offer some trade-off. As the tracking accuracy increases, the speed will be affected and vice-versa. Thus, this paper presents a novel approach for tracking moving objects which provides the most optimal trade-off in terms of its accuracy and speed. In this paper, a novel computational algorithm for dominant color to deal with the changes of objects' viewpoints is presented. Though, color cue alone is insufficient to provide a reliable tracking since in realistic environment, specifically outdoors, variation of illumination may affect the object appearance. Additional feature that is invariant to this imaging feature, namely edge is used in this project to compensate with the shortcoming. The tracking performance of the algorithm based on each individual feature and the fusion of the edge and color features are presented. Experimental results show that the proposed algorithm is reliable for real-time operation