Hierarchical Spatial–Temporal UAV Tracking With Three-Dimensional Wavelets for Road Traffic Surveillance

Visual tracking plays a vital role in modern intelligent transportation systems (ITSs) to sense traffic environments and trace targets, wherein uncrewed aerial vehicles (UAVs) are commonly used for data collection. Specifically, UAV tracking is particularly needed in road traffic surveillance (RTS) systems due to the mobility of UAV and the complexity of road traffic environments. Currently, many existing trackers leverage spatial-temporal features to increase their tracking capabilities. However, the utilization of spatial-temporal features normally involves considerable extra network modules and time-consuming recursive deduction processes, making these trackers impractical in ITSs. To address the issue of low efficiency, we propose FWTrack, a novel tracker that constructs hierarchical spatial-temporal features via three-dimensional wavelets and thus achieves efficient spatial-temporal visual tracking. FWTrack employs the spatial wavelets to reinforce its feature extraction ability in an approximately parameter-free manner and applies the temporal wavelets to adaptively separate static backgrounds from the attention modules of FWTrack, thereby speeding up the model. Moreover, the window-wise attention technique, is adopted and enhanced in FWTrack to further increase its efficiency. Thus, FWTrack can be readily deployed in UAVs for RTS purposes. Extensive experiments conducted on seven benchmarks demonstrate that FWTrack achieves state-of-the-art tracking accuracy and efficiency.