Abstract
Recently, we have been concerned with locating and tracking vehicles in aerial videos. Vehicles in aerial videos usually have small sizes due to use of cameras from a remote distance. However, most of the current methods use a fixed bounding box region as the input of tracking. For the purpose of target locating and tracking in our system, detecting the contour of the target is utilized and can help with improving the accuracy of target tracking, because a shape-adaptive template segmented by object contour contains the most useful information and the least background for object tracking. In this paper, we propose a new start-up of tracking by clicking on the target, and implement the whole tracking process by modifying and combining a contour detection network and a fully convolutional Siamese tracking network. The experimental results show that our algorithm has significantly improved tracking accuracy compared to the state-of-the-art regarding vehicle images in both OTB100 and DARPA datasets. We propose utilizing our method in real time tracking and guidance systems.
Highlights
Visual object tracking is fundamental in various tasks of computer vision, such as video surveillance [1], augmented reality, or autonomous and assistance systems, such as automatic driving [2]
A large amount of previous work has been done in both single object tracking [3,4,5,6,7] and multiple-object tracking [8,9,10,11,12,13,14]
There are different kinds of challenges in object tracking, such as appearance variance caused by motion, illumination, occlusion, and deformation [15,16]
Summary
Visual object tracking is fundamental in various tasks of computer vision, such as video surveillance [1], augmented reality, or autonomous and assistance systems, such as automatic driving [2]. Tracking generally describes the task of detecting and following one or more than one objects in a video sequence, where substantial strategies are used. There are different kinds of challenges in object tracking, such as appearance variance caused by motion, illumination, occlusion, and deformation [15,16]. The drifting problem may be caused by several reasons: target occlusion, articulated or non-rigid motions, confusion of foreground and background, etc. Take confusion of foreground and background for an example, with only a bounding box region in the first frame as the known target, Sensors 2019, 19, 514; doi:10.3390/s19030514 www.mdpi.com/journal/sensors
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