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Speed Estimation of Multiple Moving Objects from a Moving UAV Platform

1
Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA
2
Lab of Remote Sensing Image Processing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 259; https://doi.org/10.3390/ijgi8060259
Received: 14 March 2019 / Revised: 16 May 2019 / Accepted: 26 May 2019 / Published: 31 May 2019
(This article belongs to the Special Issue Deep Learning and Computer Vision for GeoInformation Sciences)
PDF [2777 KB, uploaded 31 May 2019]

Abstract

Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.
Keywords: multiple object speed detection; faster R-CNN; discriminative correlation filter; feature based image alignment; structural similarity index measure multiple object speed detection; faster R-CNN; discriminative correlation filter; feature based image alignment; structural similarity index measure
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Biswas, D.; Su, H.; Wang, C.; Stevanovic, A. Speed Estimation of Multiple Moving Objects from a Moving UAV Platform. ISPRS Int. J. Geo-Inf. 2019, 8, 259.

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