Dataset Information

Robust UAV-based Tracking Using Hybrid Classifiers.

ABSTRACT: Visual object tracking is a key factor for unmanned aerial vehicles (UAVs). In this paper, we propose a robust and effective visual object tracking method with an appearance model based on the locally adaptive regression kernel (LARK). The proposed appearance model encodes the geometric structure of the target. The tracking problem is formulated as two binary classifiers via two support vector machines (SVMs) with online model update. The backward tracking which tracks the target in reverse of time is utilized to evaluate the accuracy and robustness of the two SVMs. The final locations are adaptively fused based on the results of the forward tracking and backward tracking validation. Several state-of-the-art tracking algorithms are evaluated on large scale benchmark datasets which include challenging factors such as heavy occlusion, pose variation, illumination variation and motion blur. Experimental results demonstrate that our method achieves appealing performance.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6456073 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Robust UAV-based Tracking Using Hybrid Classifiers.

Wang Yong Y Shi Wei W Wu Shandong S

Machine vision and applications 20180929 1

Visual object tracking is a key factor for unmanned aerial vehicles (UAVs). In this paper, we propose a robust and effective visual object tracking method with an appearance model based on the locally adaptive regression kernel (LARK). The proposed appearance model encodes the geometric structure of the target. The tracking problem is formulated as two binary classifiers via two support vector machines (SVMs) with online model update. The backward tracking which tracks the target in reverse of t ...[more]

PMID: 30983700

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Project description:Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPARf), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPARf models had R2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPARf during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPARf model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPARf and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton.

Dataset Information

Robust UAV-based Tracking Using Hybrid Classifiers.

Publications

Robust UAV-based Tracking Using Hybrid Classifiers.

Similar Datasets

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