Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.

Unmanned Aerial Vehicles (UAVs) have been widely applied in military and civilian fields due to their flexibility and effectiveness. As a vital component of UAVs, the vision system has taken on great significance in different applications (e.g., autonomous landing, traffic surveillance, and disaster rescue) to attract widespread attention in recent years. Therefore, the automatic understanding of visual data collected from these air platforms becomes urgently needed in UAV systems. In this review, we revisit and summarize the recent techniques and developments for several typical UAV applications, including object detection, object tracking, and semantic segmentation. In addition, we also highlight the difficulties and subsequent orientations from different perspectives, which may stimulate future research and applications in the UAV vision era.

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