Adaptive Image Dehazing and Object Tracking in UAV Videos Based on the Template Updating Siamese Network

Abstract

Visual object tracking for unmanned aerial vehicles (UAVs) is extensively used in civil and military applications such as intelligent transportation, disaster rescue, and military reconnaissance. However, when a UAV encounters haze weather, its tracking performance deteriorates greatly because of problems such as blurring and quality degradation of the collected images. Most trackers are designed to operate in good weather conditions, this greatly limits the robustness of UAV tracking in haze weather. To solve this problem, this paper proposes an adaptive dehazing Siamese network, which consists of an adaptive dehazing module, a dynamic template update branch, and feature extraction and bounding box prediction subnetworks. The dehazing module yields haze-free images and preserves the edge information of the object, so that the tracker can extract the object features with better characterization. Subsequently, to mitigate the risk of tracking drift caused by the changing appearance of the object, the dynamic template update branch dynamically selects more reliable template images during tracking. In addition, to solve the problem of insufficient UAV object tracking testing datasets in haze scenes, a haze testing dataset for UAV video object tracking is obtained through artificial synthesis and data collection in real-world haze scenes. Furthermore, experiments are conducte on haze testing datasets, namely UAV123-AH, UAV20L-AH, UAVDT-AH, DTB70-AH, and UAVhaze; the results verified that the proposed tracker yields more competitive performance and more robustness in haze scenes compared with the existing algorithms.