Abstract
Abstract. Object tracking has gained much attention in the field of computer vision and intelligent traffic analysis. Satellite videos are more suitable for long-distance tracking comparing to the road traffic videos. However, most of the state-of-the-art methods produce poor results when applied to satellite videos, due to low resolution of the small target and interference from similar background in satellite videos. In this paper, we present an improved Discriminative Correlation Filter based approach specifically tailored for small objects tracking in satellite videos through applying spatial weight in the filter and estimating the pose by Kalman filter. First, a spatial mask is introduced to encourage the final filter to give different contributions depending on the spatial distance. Furthermore, Kalman filter is incorporated into the approach with the aim of predicting the position when the target run into the large area similar background region. Finally, an efficient strategy for combining the improved DCF tracker and pose estimation is proposed. Experimental results on three satellite videos describing the traffic conditions of three cities demonstrate that the proposed approach can effectively track the targets even though the targets are similar to the background region in a period of time. Compare with other state-of-the-art methods, the final accuracies and speeds of our method achieve the best.
Highlights
Object tracking is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks
Single channel is used in the correlation filters, seeing that targets in satellite video have a low resolution and their colors are similar to the backgrounds
To alleviate the problem induced by the similar background, we introduce a spatial weight mask m
Summary
Object tracking is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. The generative model treats the tracking as a searching problem to find a region most similar to the target These methods mainly use the information of the target itself such as color, position, feature corners, etc., ignoring the background information. Color Attribute(Danelljan, Khan, Felsberg, & Van De Weijer, 2014) is introduced to rich the feature, DSST(Danelljan, Häger, Shahbaz Khan, & Felsberg, 2014) is proposed to solve the problem of scale variations, Spatial Regularization(Danelljan, Hager, Khan, & Felsberg, 2015) is used to reduce the boundary effects Most of these methods fail to track the targets effectively when applied to the satellite video, as the background information with target-similar color in the searching box hampers accurate location of the target.
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