Extraction of Vehicle Groups in Airborne Lidar Point Clouds with Two-Level Point Processes

In this paper we present a new object based hierarchical model for joint probabilistic extraction of vehicles and groups of corresponding vehicles - called traffic segments - in airborne Lidar point clouds collected from dense urban areas. Firstly, the 3-D point set is classified into terrain, vehicle, roof, vegetation and clutter classes. Then the points with the corresponding class labels and echo strength (i.e. intensity) values are projected to the ground. In the obtained 2-D class and intensity maps we approximate the top view projections of vehicles by rectangles. Since our tasks are simultaneously the extraction of the rectangle population which describes the position, size and orientation of the vehicles and grouping the vehicles into the traffic segments, we propose a hierarchical, Two-Level Marked Point Process (L2MPP) model for the problem. The output vehicle and traffic segment configurations are extracted by an iterative stochastic optimization algorithm. We have tested the proposed method with real data of a discrete return Lidar sensor providing up to four range measurements for each laser pulse. Using manually annotated Ground Truth information on a data set containing 1009 vehicles, we provide quantitative evaluation results showing that the L2MPP model surpasses two earlier grid-based approaches, a 3-D point-cloud-based process and a single layer MPP solution. The accuracy of the proposed method measured in F-rate is 97% at object level, 83% at pixel level and 95% at group level

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality--vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces

Sustainable natural resource management in regional ecosystems : case studies in semi-arid and humid regions

Sustainability calls for policies that meet current societal needs without compromising the needs of future generations; thus, a dual relationship between human and natural resources is required. The main goal of the current research was to introduce up-to-date environmental techniques for sustainable natural resource utilization in semiarid and humid ecosystems in short and long term. To achieve this goal, two studies were implemented. First, sustainable land use management was evaluated in a newly reclaimed, semiarid region in the Bustan 3 area (341.27 km2), Egypt. To achieve sustainable management in this agro-ecosystem; detection of land cover change, assessment of the most sensitive areas to desertification, and evaluation of land capability for agricultural use were required. Using multi-temporal remotely-sensed data in the Bustan 3, the results indicated that this area had been drastically changed from 100% barren desert land to 79% agricultural land, due to successful land reclamation efforts in the 1990s. Although 70% of this area had a good capability for agricultural production, ¡­89% of the Bustan 3 area was critically sensitive to desertification. By applying suitable land management scenarios, the land capability for agricultural use could be increased. Second, a natural resource conservation program was examined by studying the effects of compost/mulch, as a best management practice, for soil erosion control on highway roadsides in Louisiana, USA (a humid region). Louisiana is plagued by widespread impairments to surface water quality. Total suspended solids (TSS) and associated turbidity in runoff water are considered the most problematic nonpoint source pollutant of Louisiana surface waters. At the plot scale, the effects of compost/mulch on soil and water resources were evaluated. Research results showed that the use of compost/mulch without tillage incorporation successfully conserved the topsoil on the roadsides, increased soil moisture retention, moderated soil temperature, and reduced the TSS, soil loss, runoff, and water flow rate. Tillage incorporation is not recommended since it decreased the compost/mulch effectiveness in reducing runoff and sediment losses. While the two studied areas, in semiarid and humid regions, were disparate in their characteristics, sustainable natural resource management was successfully achieved by using appropriate management practices in each case

Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use

Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean

Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system

Methodologies and Applications of Data Coordinate Conversion for Roadside LiDAR

Light Detection and Ranging (LiDAR) is becoming more popular in applications of the transportation field, including traffic data collection, autonomous vehicles, and connected vehicles. Compared with traditional methods, LiDAR can provide high-resolution-micro-traffic data (HRMTD) for all road users without being affected by the light condition. Unlike the macro data collected by traditional sensors containing traffic flow rates, average speeds, and occupancy information, the HRMTD can provide higher accuracy and more detailed multimodal all-traffic trajectories data. But there are still some limitations when using it. The first one is that the raw data is in LiDAR’s coordinate system, which greatly affects the visibility of the data. Secondly, the detection range limits its further development. Although LiDAR can detect data within 200 m from itself, the effective detection range is 50~ 60 m. What’s more, the occlusion issue occurred from time to time.To overcome these limitations, data mapping and integration methods are needed. This research proposed the data integration and mapping method for roadside LiDAR sensors. There is a total of six main steps in this method: reference points collection, reference points matching, transformation matrix calculation, time synchronization, data integration, and data mapping. The raw LiDAR data is in the Cartesian coordinate system. In this coordinate system, the position of each LiDAR point is represented by (x,y,z). To map these points on the GIS-based software based on the WGS 1984 coordinate system, the coordinate system of the LiDAR data should be transformed. After converting the LiDAR data into Geographic Coordinate Systems, the ICP method is applied to integrate the data collected by multiple LiDAR sensors. Compared with the original LiDAR data, the longitude, latitude, and elevation information are added to the processed dataset. The new dataset can be used as the input for the HRMTD processing procedures for roadside LiDAR. Other than benefiting the autonomous vehicle(AV) system and connected vehicle(CV) system, the HRMTD can also serve other transportation applications. This research provides an application using the HRMTD obtained from roadside LiDAR data to extract lane and crosswalk-based multimodal traffic volumes. This method has three main steps: start and endpoint selection, detection zone selection, and threshold learning. The second step is the primary step of the method, which can be divided into four sub-steps: location searching, data comparison, size searching, and best zone selection. A whole day of data collected in the real world is used to verify the method and compared with the manually counted traffic volume, and the result shows that the accuracy of this traffic volume extraction method reaches 95% or higher. This research will significantly change how traffic agencies assessing road network performance and add great traffic values to the existing probe-vehicle data and crowd-resourced data