# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 1 Introduction  
The objective of object tracking is to estimate the exact location and size of an object of interest in an image over time, where the object can be more accurately tracked if only the areas with a high probability of object existence are considered in the image. Subsequently, conventional visual trackers have employed object proposal methods [1]. The goal of object proposal was to determine if object regions exist given an image and, if they exist, to estimate positions and scales of these object regions. To localize the object regions, conventional object proposal methods [2] search for a set of bounding boxes of object candidate regions instead of examining all positions in an image. However, these object proposal methods are very sensitive to complex background clutter in the image and cannot accurately propose areas of small-sized objects. Moreover, if the objects and their background share similar appearances, conventional approaches frequently propose the background as the object area. Consequently, the visual tracking accuracy reduces owing to inaccurate object proposals.  
To overcome this issue, we propose a visual tracking method that can track small unmanned aerial vehicles (UAVs) [3, 4, 5, 6, 7, 8, 9, 10, 11] using a new object proposal algorithm. To make object proposals robust to various factors that interfere with visual tracking, we conduct object proposal voting (Figure 0(b)) where areas with high probabilities of objects being presented have more votes, whereas areas where objects are unlikely to exist have fewer votes. Based on these proposal results, a small UAV can be accurately tracked under various challenging visual tracking environments. For example, as shown in Figure 0(a), the target object marked with a red circle is very small and its appearance is very similar to that of its background. Thus, conventional methods easily propose background areas as object areas, as shown in the green boxes of Figure 0(c). In contrast, our method generates the object proposal voting image in Figure 0(b), where object regions have more votes, which produce areas in the image that are brighter than the background regions. By thresholding the object proposal voting image, our method proposes a single object area, as shown inthe blue box in Figure 1d. Based on the estimated object region, our method can accurately track the target.  
The contributions of our method are summarized as follows:  
* We propose a novel object proposal algorithm called object proposal voting (OPV). As a result that voting strategies are typically insensitive to noise, the proposed OPV is robust to background clutter and can detect small-sized objects.
* We present a visual tracking system based on the proposed OPV to track small-sized UAVs, such as drones in real-world environments.  
The remainder of this paper is organized as follows. We relate conventional object proposal methods with the proposed method in Section 2. Section 3 describes object proposal baselines, while Section 4 proposes a novel OPV method. In Section 5, we present a new visual tracking method based on the proposed OPV. Section 6 describes experimental results and Section 7 concludes our paper.