Global-Local and Occlusion Awareness Network for Object Tracking in UAVs

Abstract

Multi-object tracking in unmanned aerial vehicle (UAV) scenes is a crucial task with numerous applications across various domains. The goal of this task is to track multiple objects such as people and vehicles over time as they move through the scene, often captured by cameras mounted on UAV. However, tracking objects in UAV scenes can be challenging due to several factors, including the scale variance of objects as they move through the scene and the frequent occlusions caused by complex scenes. To address these issues, we propose a Global-Local and Occlusion Awareness (GLOA) tracking network for unmanned aerial vehicles. It comprises two main components: global-local awareness detector (GLA-D) and the occlusion awareness data association (OADA). The GLA-D uses our specially designed global-local awareness block to extract scale variance feature information from the input frames. It then outputs more discriminative identity information by adding identity embedding branches to the prediction head. The GLA-D is designed to better handle scale variance issues and improve object tracking accuracy. The OADA method used different metrics for high and low scoring detection frames was to alleviate occlusion problems in tracking scenarios. By combining these two components, the GLOA provides a more robust and effective solution for multi-object tracking in UAV scenes. Experiments on two public datasets may indicate the effectiveness of the proposed method.

An Occlusion-Aware Tracker With Local-Global Features Modeling in UAV Videos

“…While the majority of target tracking networks primarily focus on short-term tracking, it is imperative to address the challenges associated with long-term target tracking to accurately detect and locate targets. Long-term target tracking encounters greater variations in targets and more frequent occlusions compared to short-term tracking [21]. Existing methods effectively tackle the issue of target changes by incorporating spatio-temporal context information [22], [23]; however, they face challenges such as tracking drift or losing the target when dealing with frequent occlusions.…”