Fariborz Ghorbani; Hamid Ebadi; Masoud Varshosaz
Abstract
Extended Abstract Introduction In the past few decades, urban environments have expanded much larger than before. One of the most important problems in most metropolises and even small cities is the management of the transportation system. An advanced monitoring system of urban vehicles ...
Read More
Extended Abstract Introduction In the past few decades, urban environments have expanded much larger than before. One of the most important problems in most metropolises and even small cities is the management of the transportation system. An advanced monitoring system of urban vehicles allows for overcoming the traffic problems. With the development of unmanned aerial vehicles (UAVs), continuous and accurate monitoring of urban environments has been provided for the users. In this research, an efficient method is presented that detects the vehicle in the UAV images. The proposed method is effective in terms of computational speed and accuracy. Materials and Methods The foundation of the proposed method is based on the characteristics of the local features in the UAV images. The presented method consists of two main stages of training classification model and detecting vehicles. In the first part, local features are extracted and described by the SIFT algorithm. The SIFT algorithm is one of the most powerful algorithms for extracting and describing local features that are used in various photogrammetry and machine vision applications. This algorithm is robust to geometric and radiometric changes of the images. Due to the high dimensions of extracted features from all the training samples, the BOVW (Bag of Visual Word) model has been applied. This model is used to reduce the dimensions of the features and display the images. Simple and efficient computing is one of the significant features of the BOVW model. At this stage, after producing a library of features, the SVM classification model is trained. In the detection part of the algorithm, the images are entered into the algorithm and the local features are extracted in all images by the SIFT algorithm. The BOVW model is often used to display an image patch. In most researches, this model is implemented by applying a search window to the whole image. This type of methods has a higher confidence level in detection, but it is a very time consuming process and increases the volume of the computations. For this purpose, the approach of points clustering and their representation by the BOVW model is proposed. In this method, features that are within a certain range are considered as a cluster. Euclidean distance is used in image space for clustering. Then, the clusters produced by the BOVW model are displayed. Then, a feature vector is constructed for each cluster. The trained SVM is applied to each of the production vectors and each cluster is classified as a vehicle and non-vehicle. If the cluster is detected as a car, the position of the center of that cluster is marked on the image. Results and discussion The proposed method was implemented on 8 images with a number of different car targets. Also, considering the use of the search window approach in many researches, our results were compared with the results obtained by other researchers. The results show that the calculation time of the proposed method is 82 seconds, while the search window method takes 2496 seconds to run. In order to verify the accuracy of the algorithm, two criteria were used. The first criterion is the “Producer's accuracy”, which represents the proportion of correct detections of the vehicle to the entire vehicles existing in the images. This criterion is 75.79% for the proposed method. The second criterion is the “User's accuracy”. This criterion is obtained by dividing the correctly detected samples into the sum of the correctly and incorrectly detected samples. The User's accuracy criterion has been reported to be 59.50%. Conclusion The value of the Producer's accuracy criterion is greater for the search window method which has led to a more accurate detection of vehicles compared to our method. This is due to the small moving steps of the search window in the entire image. However, the search window method has increased the amount of the time spent on the calculations. The User's accuracy criterion shows that the proposed method has less incorrect detections. The results indicate that our method has a higher degree of reliability. The average of these two criteria indicates the superiority of the proposed method in terms of the accuracy of the calculations. On the other hand, the proposed algorithm has a great advantage in terms of computational speed compared to the search window method.
Alireza Safdarinezhad; Mahdi Mokhtarzadeh; Mohammadjavad Valadanzouj
Abstract
Abstract
3D point clouds obtained by Airborne Laser Scanner Systems provide a varied and unique geometric information of the physical terrain surfaces due to advantages such as relatively high geometric accuracy and high spatial density of the points. Classification ...
Read More
Abstract
3D point clouds obtained by Airborne Laser Scanner Systems provide a varied and unique geometric information of the physical terrain surfaces due to advantages such as relatively high geometric accuracy and high spatial density of the points. Classification and separation of cloud point data to environmental constructive terrains plays an important role in the process of 3D modeling of terrains. In this procedure, point cloud clustering is a fundamental step in the procedure of information extraction form LiDAR's data. In this paper, a novel method is proposed for supervised classification of LiDAR cloud of points based on contextual analysis of LiDAR points. The proposed method consists of three main steps. In the first step, a set of features based on contextual analyses are produced for each point in LiDAR data. In the second step, the optimum feature selection is done in the modified prototype space using a new strategy. The last step is conducted by a simple k-means clustering in the feature space spanned by optimum contextual clusters. An urban area with the residential texture has been used as the case study to evaluate the proposed method. The results indicate proper classification accuracies. The overall accuracies and kappa coefficients were 93.15% and 0.89 respectively.
Elham Ghasemifar; Somayyeh Naserpoor
Volume 23, Issue 89 , May 2014, , Pages 54-60
Abstract
Zoning by climatic elements and factors is one of the most crucial issues which is of interest due to its importance in agriculture and architecture. Climate is the result and function of dominant elements and factors in the area. In this article, zoning is performed based on monthly temperature and ...
Read More
Zoning by climatic elements and factors is one of the most crucial issues which is of interest due to its importance in agriculture and architecture. Climate is the result and function of dominant elements and factors in the area. In this article, zoning is performed based on monthly temperature and precipitation average in 16 synoptic stations of Zagros area using 4 analytical methods - analyzing the main component, seasonal Z-score of temperature and precipitation, standard deviation of monthly and seasonal temperature, and climatic coefficients (De Martonne’s aridity index and Peggy climogram). These selected stations possess the most complete statistics since establishment in 2005. Z score results were verified using variance analysis. In the first 3 methods, zoning was performed using Ward’s method. 3 main components and 5 areas were identified which justify 91.84 percent of variable variances. Z score shows 5 areas in spring and autumn, and 4 areas in summer and winter for rainfall, and 4 areas in summer and 3 areas in other seasons for temperature. Variance analysis test proved the hypothesis (inequality of the areas). 5 main areas were reached based on the temperature data. Applying De Martonne aridity index and Peggy climogram, 3 and 4 areas were verified respectively. Finally, maps of precipitation and temperature areas of Zagros were produced by Inverse Distance Weighted method in GIS environment.
