Development Of Algorithms For Vehicle Classification And Speed Estimation From Dynamic Scenes By On-Board Camera Using Image Processing Techniques

Vehicle assistance system applications benefit the drivers and passengers to promote better and safer driving situations. In terms of usability of dash camera, most vehicle owners pre­ installed the camera as a personal safety purpose to record the path they went through. The wide availability of various models of the dash cameras on the market, however, lacks in intelligence to process the information that can be obtained from the camera technology system itself. Moreover, in most studies for Intelligence Transport System (ITS), the implementation of static camera, for example CCTV, is popular thus, it is an encouragement for improvement to develop a vehicle assistance system using dynamic camera scenes. The main purpose of this research was to develop a vehicle detection, vehicle classification, and vehicle speed estimation system in dynamic scenes fully by image processing technique. The scope of this research covered Malaysia highway in Skudai, Johor; Ayer Keroh, Melaka and Kajang, Selangor. Video database of these highway areas was recorded by the on-board camera unit placed on the front dashboard area of the host vehicle. Image dataset was collected with positive image sets containing four vehicle classes namely car, lorry, bus, and motorcycle. It was decided that the technique for vehicle detection were Haar-Like and Cascade Classifier while vehicle classification was based on the ratio characteristics of the vehicle detected. The use of ratio value was an added advantage for the classification process since the prepared image dataset were based on each vehicle class dimension and the ratio value are the uniqueness property for each vehicle class. Speed estimation of the vehicle started with host vehicle speed estimation by lane detection technique since the road lane was the most consistence moving object inside the video region. The Host vehicle distance measurement used the broken lane detection and for a scale factor calculation, the width of the highway lanes was calculated by measuring the lane width inside the image and calibrated with real value in meter of the lanes stated by (Jabatan Kerja Raya, 1997). Detected vehicle speed measurements were based on its centroid tracking measurements. Result analysis on accuracy measurement in vehicle detection system obtained 0.93 true positive rates from 300 vehicles presented in the video data. Further analysis in vehicle classification was proved to obtain true positive rate of 0.98 for car class, 0.89 for lorry class, 0.89 for bus class, and 0.75 for motorcycle class. For analysis of speed estimation achieved with the average percentage 6.42% for speed error of host vehicle tested on 10 different videos. In detected vehicle, it speed estimations were based on the host vehicle speed calculation by observation its position and motion behavior in comparison with the host vehicle speed value. Overall the e development indicated that image processing has the ability to visualize the surrounding area for drivers and passengers that was near to real human visions a contribution to human-machine interactions that can be beneficial

The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: http://www.highD-dataset.comComment: IEEE International Conference on Intelligent Transportation Systems (ITSC) 201

Improving construction materials management practices in construction sites

Construction Materials Management is a vital function for improving productivity in construction projects. Poor materials management can often affect the overall construction time, quality and budget. Currently, the construction material management practice in Somalia is believed to be poorly performed. Lack of standardized construction materials management system is one of the key issues facing by the building industry in Mogadishu-Somalia. The aim of this study was to investigate the current practices of material management at construction sites in Mogadishu-Somalia. A questionnaire survey study design was used to explore construction materials management practices. Fifty questionnaires were distributed to project managers, project engineers, site engineers, engineer, and foreman, and they were received and analysed. The following data analysis techniques were used: descriptive statistics were conducted to report sample characteristics, reliability and validity analyses were performed to confirm robustness of the instrument, graphical presentation such as bar charts were developed, and finally Average Mean Index Scale were constructed. The study results reveals that, 46.7% of respondent’s organization obtain materials for sites without site requisition by site engineer provisions, while 28.9% of respondent’s organization procure materials for sites with site requisition by project manager provisions and 13.3% of respondent’s organization procure materials for site by engineer. The results indicated that currently there is no standardized and computerized construction materials management system applied in Somalia. The researcher concluded that all contracting companies are interested in using some techniques of managing construction materials such as creating and updating database for materials categories from local and international suppliers. Finally, researcher recommends to use computerized construction materials management systems to reduce effort and time, and to achieve more accurate results

Intergrated leachate treatment by sequencing batch reactor (SBR) and micro-zeolite (MZ)

Biological treatment has a lot of potential in leachate treatment with the ability of the biodegradable substrates and this method can reduce the cost of treatment residues with respect to ecological and economical requirements.The aims of this study are to investigate the effect of biological treatment by using sequencing batch reactor (SBR) system in different condition consisting anaerobic (An), anoxic (Ax), and oxic (Ox) with different reaction time. An integration in combining phases consisting An/Ax/Ox is used in order to achieve maximum removal. Then, followed by the performance of combination phases consisting An/Ax/Ox in SBR system with addition of adsorption adsorbent micro-zeolite (MZ) (size range 75-150 μm) at different dosages. The raw leachate and sludge were collected from sanitary landfill from Tanjung Langsat, Pasir Gudang. An condition has better performance in an SBR system at optimum reaction time 11 hr promoting the percentage removal efficiency of chemical oxygen demand (COD), ammonia nitrogen (AN), total nitrogen (TN), total phosphorus (TP) and suspended solid (SS) and turbidity which were 77%, 74.65%, 75.07%, 76.05%, 63.91%, and 62.67% respectively. The result indicated that the combined condition consisting An/Ax/Ox at the optimum time reaction of each condition gives the removal efficiency COD, AN, TN, TP, SS, and turbidity which were 85.78%, 88.65%, 87.07%, 86.9%, 81.92% and 81.15% respectively. The application addition of adsorption adsorbent gives optimum dosage at 5 g/L. The addition of MZ shows good removal efficiency which were more than 90% at overall parameter

A disaster risk assessment model for the conservation of cultural heritage sites in Melaka Malaysia

There exist ongoing efforts to reduce the exposure of Cultural Heritage Sites (CHSs) to Disaster Risk (DR). However, a complicated issue these efforts face is that of ‘estimation’ whereby no standardised unit exist for assessing the effects of Cultural Heritage (CH) exposed to DR as compared to other exposed items having standardised assessment units such as; ‘number of people’ for deaths, injured and displaced, ‘dollar’ for economic impact, ‘number of units’ for building stock or animals among others. This issue inhibits the effective assessment of CHSs exposed to DR. Although there exist several DR assessment frameworks for conserving CHSs, the conceptualisation of DR in these studies fall short of good practice such as international strategy for disaster reduction by United Nations which expresses DR to being a hollistic interplay of three variables (hazard, vulnerability and capacity). Adopting such good practice, this research seeks to propose a mechanism of DR assessment aimed at reducing the exposure of CHSs to DR. Quantitative method adopted for data collection involved a survey of 365 respondents at CHSs in Melaka using a structured questionnaire. Similarly, data analysis consisted of a two-step Structural Equation Modelling (measurement and structural modelling). The achievement of the recommended thresholds for unidimensionality, validity and reliability by the measurement models is a testimony to the model fitness for all 8 first-order independent variables and 2 first-order dependent variables. While hazard had a ‘small’ but negative effect, vulnerability had a ‘very large’ but negative effect on the exposure of CHSs to DR. Likewise, capacity had a ‘small’ but positive effect on the exposure of CHSs to DR. The outcome of this study is a Disaster Risk Assessment Model (DRAM) aimed at reducing DR to CHSs. The implication of this research is providing insights on decisions for DR assessment to institutions, policymakers and statutory bodies towards their approach to enhancing the conservation of CHSs

Multi-Lane Perception Using Feature Fusion Based on GraphSLAM

An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data for the algorithm. The information is accumulated and fused using GraphSLAM and the result constitutes the basis for a multilane clothoid model. To allow incorporation of additional information sources, input data is processed in a generic format. Evaluation of the method is performed by comparing real data, collected with an experimental vehicle on highways, to a ground truth map. The results show that ego and adjacent lanes are robustly detected with high quality up to a distance of 120 m. In comparison to serial lane detection, an increase in the detection range of the ego lane and a continuous perception of neighboring lanes is achieved. The method can potentially be utilized for the longitudinal and lateral control of self-driving vehicles