Abstract
Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion. A new model was developed by combining compact and adaptive search region (SR). The model can accurately and robustly track MAVs with a fast computation speed. A compact SR, which is slightly larger than a target MAV, is less likely to include a distracting background than a large SR; thus, it can accurately track the MAV. Moreover, the compact SR reduces the computation time because tracking can be conducted with a relatively shallow network. An optimal SR to MAV size ratio was obtained in this study. However, this optimal compact SR causes frequent tracking failures in the presence of the dynamic MAV motion. An adaptive SR is proposed to address this problem; it adaptively changes the location and size of the SR based on the size, location, and velocity of the MAV in the SR. The compact SR without adaptive strategy tracks the MAV with an accuracy of 0.613 and a robustness of 0.086, whereas the compact and adaptive SR has an accuracy of 0.811 and a robustness of 1.0. Moreover, online tracking is accomplished within approximately 400 frames per second, which is significantly faster than the real-time speed.
Highlights
Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion
Online tracking is accomplished within approximately 400 frames per second, which is significantly faster than the real-time speed
TheThe algorithm thenthen startsstarts to track a target from startingpoints pointsare areselected selected every frames
Summary
Tracking a micro aerial vehicle (MAV) is challenging because of its small size and swift motion. An object detector is used to estimate the location, size, and class of objects from a single image The deformable part model [4] involves a sliding window approach for object detection This requires a long computational time for sliding the window throughout the entire image. An R-CNN is a two-stage detector; the first stage detects regions of interest in images, whereas the second stage determines the accurate location, size, and object class. This approach greatly improved the mean average precision of detections, but still required a long computational time. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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