Robust Correlation Tracking for UAV Videos via Feature Fusion and Saliency Proposals

Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking infrared drone targets difficult and challenging. This work proposes a simple and effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. Concretely, SiamSTA builds a two-stage re-detection network to predict the target state using the template of first frame and the prediction results of previous frames. To tackle the challenge of small-scale UAV targets for long-range acquisition, SiamSTA imposes spatial and temporal constraints on generating candidate proposals within local neighborhoods to eliminate interference from background distractors. Complementarily, in case of target lost from local regions due to fast movement, a third stage re-detection module is introduced, which exploits valuable motion cues through a correlation filter based on change detection to re-capture targets from a global view. Finally, a state-aware switching mechanism is adopted to adaptively integrate local searching and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on three anti-UAV datasets nicely demonstrate SiamSTA’s advantage over other competitors. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.

Visual Object Tracking for Unmanned Aerial Vehicles Based on the Template-Driven Siamese Network

Visual object tracking for unmanned aerial vehicles (UAV) is widely used in many fields such as military reconnaissance, search and rescue work, film shooting, and so on. However, the performance of existing methods is still not very satisfactory due to some complex factors including viewpoint changing, background clutters and occlusion. The Siamese trackers, which offer a convenient way of formulating the visual tracking problem as a template matching process, have achieved success in recent visual tracking datasets. Unfortunately, these template match-based trackers cannot adapt well to frequent appearance change in UAV video datasets. To deal with this problem, this paper proposes a template-driven Siamese network (TDSiam), which consists of feature extraction subnetwork, feature fusion subnetwork and bounding box estimation subnetwork. Especially, a template library branch is proposed for the feature extraction subnetwork to adapt to the changeable appearance of the target. In addition, a feature aligned (FA) module is proposed as the core of feature fusion subnetwork, which can fuse information in the form of center alignment. More importantly, a method for occlusion detection is proposed to reduce the noise caused by occlusion. Experiments were conducted on two challenging benchmarks UAV123 and UAV20L, the results verified the more competitive performance of our proposed method compared to the existing algorithms.