Vehicle Detection In Remote Sensing Images
Mohammed A.-M. Salem1, Sultan Almotairi2

1Mohammed A.-M. Salem, Faculty of Media Engineering and Technology, German University in Cairo, Egypt. Faculty of Computer and Information Sciences, Ain Shams University, Egypt.
2Sultan Almotairi, Department of Natural and Applied Sciences, Faculty of Community College, Majmaah University, Majmaah, 11952, Saudi Arabia.
Manuscript received on 25 August 2019. | Revised Manuscript received on 19 September 2019. | Manuscript published on 30 September 2019. | PP: 928-933 | Volume-8 Issue-11, September 2019. | Retrieval Number: K18070981119/2019©BEIESP | DOI: 10.35940/ijitee.K1807.0881119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we considered simple steps in the proposed algorithm for optimization. It follows the machine learning pipeline such as preprocessing, sensing, feature extraction, training and classification. In preprocessing the images are thresholded iteratively in multiple color spaces to extract the candidate regions of interest (ROI). The algorithm relies on point and shape features using fast techniques in the feature extraction. The features are then clustered by the K-means algorithm and represented by the resulted clusters’ centers. Region based linear classification is finally applied using SVM to classify if the object is a vehicle or else. The proposed approach proved high detection and classification accuracy more than 86% and still running under the low complexity constraint..
Keywords: Unmanned Arial Vehicles, Traffic Monitoring, Image Segmentation; Machine Learning; Point features; Shape features; Speeded Up Robust Features extractor (SURF); Support Vector Machines (SVM).
Scope of the Article: