Reducing Processing Time for Histogram PMHT Algorithm in Video Object Tracking

This paper describes a novel approach for reducing the processing time of the histogram probabilistic multi-hypothesis tracker (H-PMHT) algorithm in video applications. Video data of flying vehicles is taken from surface to air, and a temporal difference-based technique is applied to video frames for meeting the intensity demands of H-PMHT algorithm. This technique also enables discrimination between objects and eliminates clutter. Variations between the structures of the standard and the improved version of H-PMHT algorithms are described. In addition, the improved H-PMPT is compared with the standard H-PMHT and another approved tracking algorithm to evaluate the performance and processing time reduction ratings

Enhancement Technique for Aerial Images

Abstract—Recently, we proposed an enhancement technique for uniformly and non-uniformly illuminated dark images that provides high color accuracy and better balance between the luminance and the contrast in images to improve the visual representations of digital images. In this paper we define an improved version of the proposed algorithm to enhance aerial images in order to reduce the gap between direct observation of a scene and its recorded image. I

Square root central difference-based FastSLAM approach improved by differential evolution

This study presents a new approach to improve the performance of FastSLAM. The aim of the study is to obtain a more robust algorithm for FastSLAM applications by using a Kalman filter that uses Stirling's polynomial interpolation formula. In this paper, some new improvements have been proposed; the first approach is the square root central difference Kalman filter-based FastSLAM, called SRCD-FastSLAM. In this method, autonomous vehicle (or robot) position, landmarks' position estimations, and importance weight calculations of the particle filter are provided by the SRCD-Kalman filter. The second approach is an improved version of the SRCD-FastSLAM in which particles are improved by a differential evolution (DE) algorithm for reducing the risk of the particle depletion problem. Simulation results are given as a comparison of FastSLAM II, unscented (U)-FastSLAM, SRCD-Kalman filter-aided FastSLAM, SRCD particle filter-based FastSLAM, SRCD-FastSLAM, and DE-SRCD-FastSLAM. The results show that SRCD-based FastSLAM approaches accurately compute mean and precise uncertainty of the robot position in comparison with FastSLAM II and U-FastSLAM methods. However, the best results are obtained by DE-SRCD-FastSLAM, which provides significantly more accurate and robust estimation with the help of DE with fewer particles. Moreover, consistency of the DE-SRCD-FastSLAM is more prolonged than that of FastSLAM II, U-FastSLAM, and SRCD-FastSLAM