Robust Moving Target Handoff in GPS-Denied Environments

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, longterm tracking due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. In this paper we propose a solution to the moving-target handoff problem in the absence of GPS. The proposed solution uses a nonlinear complimentary filter for self-pose estimation using only an IMU, a particle filter for relative pose estimation between UAS using a relative range measurement, visual target tracking using a gimballed camera when the target is close to the handoff UAS, and track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present extensive simulation results that demonstrates the effectiveness of our approach and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

Robust Moving Target Handoff in GPS-Denied Environments

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, longterm tracking due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. In this paper we propose a solution to the moving-target handoff problem in the absence of GPS. The proposed solution uses a nonlinear complimentary filter for self-pose estimation using only an IMU, a particle filter for relative pose estimation between UAS using a relative range measurement, visual target tracking using a gimballed camera when the target is close to the handoff UAS, and track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present extensive simulation results that demonstrates the effectiveness of our approach and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

Target tracking based on standard hedging and feature fusion for robot

Purpose Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion. Design/methodology/approach For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed. Findings Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods. Originality/value Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

A Real-Time Structured Output Tracker with Scale Adaption for Visual Target Tracking

The structured output tracking algorithm is a visual target tracking algorithm with excellent comprehensive performance in recent years. However, the algorithm classifier will produce error information and result in target loss or tracking failure when the target is occluded or the scale changes in the process of tracking. In this work, a real-time structured output tracker with scale adaption is proposed: (1) the target position prediction is added in the process of target tracking to improve the real-time tracking performance; (2) the adaptive scheme of target scale discrimination is proposed in the structured support to improve the overall tracking accuracy; and (3) the Kalman filter is used to solve the occlusion problem of continuous tracking. Extensive evaluations on the OTB-2015 benchmark dataset with 100 sequences have shown that the proposed tracking algorithm can run at a highly efficient speed of 84 fps and perform favorably against other tracking algorithms.

Visual Target Tracking via Online Reliability Evaluation and Feature Selection in the Framework of Correlation Filtering

Background: Although correlation filtering is one of the most successful visual tracking frameworks, it is prone to drift caused by several factors such as occlusion, deformation and rotation. Objective: In order to improve the performance of correlation filter-based trackers, this paper proposes a visual tracking method via online reliability evaluation and feature selection. Methods: The main contribution of this paper is to introduce three schemes in the framework of correlation filtering. Firstly, we present an online reliability evaluation to assess the current tracking result by using the method of adaptive threshold segmentation of response map. Secondly, the proposed tracker updates the regression model of correlation filter according to the assessment result. Thirdly, when the tracking result based on a handcrafted feature is not reliable enough, we propose a feature selection scheme that autonomously replaces a handcrafted feature used in the traditional correlation filter-based trackers with a deep convolutional feature that can re-capture the target by its powerful discriminant ability. Results: On OTB-2013datasets, the Precision rate and Success rate of the proposed tracking algorithm can reach 84.8% and 62.5%, respectively. Moreover, the tracking speed of proposed algorithm is 19 frame per second. Conclusion: The quantitative and qualitative experimental results both demonstrate that the proposed algorithm performed favorably against nine state-of-the-art algorithms.