Image Segmentation Method Selection for Vehicle Detection Using Unmanned Aerial Vehicle

Abstract

This article discusses the possibility of applying the methods of allocating super pixels in the task for detecting moving and stationary vehicles in images obtained from the unmanned aerial vehicle (UAV) which flying over roads and parking lots. The paper will also consider the specificity of images obtained when shooting with the UAV, the specificity of the image processing, and formed the requirements for segmentation algorithm applicable to the task. Author of the article has developed the application required to measure the average image processing speed in the video stream and the application for evaluating vehicles partitioning quality. This application works with the test image, on which the location of the vehicle were determined by the human. A study was conducted of the several algorithms for image segmentation: LIC, Quick Shift, Felzenszwalb-Huttenlocher, and Model based clustering algorithm. The article presents data on the speed and accuracy of the evaluation of these algorithms in the task for UAV's images segmentation. In conclusion, author has chosen methods that suitable for their use in specific application task. For image segmentation, it was decided to use two of the most appropriate method: segmentation algorithm Felzenszwalb-Huttenlocher and developed by the author earlier algorithm based on the approach to clustering model based clustering. The article also discusses possible further ways of unification super pixels containing regions with vehicles. Further work will focus on the modification, parallelization and accelerate software implementation of FHS and MBC. The author will be also investigate the question of the possibility of Markov Chains to solving the task for super pixels association to the regions and the question of the applicability of the binary classification of regions for the detection of vehicles.

Automated Vehicle Recognition with Deep Convolutional Neural Networks

“…object Localization with selective search There are two main traditional approaches for object localization in images: segmentation and exhaustive search. Segmentation tries to break a single partitioning of an image into its unique objects before any recognition (16). This is sometimes extremely difficult if there are disparate hierarchies of information in the image.…”

Vehicle Detection in Aerial Traffic Monitoring

“…High average image processing time makes MBC and Quick shift algorithms unsuitable for on-board use. SLIC algorithm is unsuitable due to a poor quality of segmentation (for a detailed comparison of these three algorithms, see Abramov et al, 2015). Comparison of FHS, SEEDS and SEEDS Revised algorithms has been conducted on operational images without regard to the ROI mask as per the following characteristics (Table 1) The (a)-(c) characteristics affect the performance of the entire vehicle detection system.…”

“…In this study a cascading approach to the vehicle recognition has been defined. The works (Kim and Chervonenkis, 2015;Abramov et al, 2015) have suggested using the image segmentation into superpixels followed by their association in regions, this approach is also used in proposed algorithm herein. These works were inspired by (Choi and Yang, 2009) paper which applies mean shift segmentation in the Luv color space in order to extract blobs (superpixels) of the image.…”