# Visual MAV Tracker with Adaptive Search Region
## 1 Introduction
CNN trackers with partial online learning consist of a CNN feature extractor and a decision network. The decision network determines the location and size of the target. For the decision network, a fully connected layer was used to score candidate samples [28]. A CNN was also used to generate a Gaussian-shaped map for this decision network [29]. While a CNN feature extractor is trained offline, the parameters of the decision network are determined by online training. Finally, CNN trackers without online learning show a fast-tracking performance because all training procedures are conducted offline. For example, a fully convolutional Siamese network was adopted for similarity learning [30]. In the offline learning phase, the Siamese network is trained to discriminate between positive and negative samples. Subsequently, in the online tracking phase, the model generates a filter using the target in the first frame. Then, it searches for similar features in subsequent frames via a pre-trained Siamese network. In contrast to previous models, the filter is not updated for subsequent frames. Thus, the computation is considerably faster than that of previous models. However, this approach is vulnerable to variations in visual aspects, deformations, and rotations of the target object owing to the absence of online learning. A subnetwork posterior to the Siamese network has been developed to improve the accuracy [31]. Specifically, the two subnetworks independently perform classification and regression tasks. A classifier and a regressor without an anchor box were added ahead of the Siamese network [32]. Because their algorithm did not use the anchor box, it is robust with regards to the size and aspect ratio of target objects and outperforms other Siamese-based trackers. In this study, a new object tracker was developed considering the advantages and limitations of previous models. The proposed model is a fully offline trained model for fast computation in online tracking, and an anchor box is excluded from the algorithm for robust tracking. A regression approach similar to R-CNN was adopted because it shows highly accurate estimations for location and size. However, R-CNN-based detectors are computationally slow because thousands of regions are investigated. Thus, only one search region (SR) is considered in this model; the target object is tracked within a single SR with a fully convolutional network (FCNN). An optimal SR for regression is selected because the tracking performance relies heavily on the SR. The scale and location of the SR are determined based on the size and position of the target object and the motion of the object. The proposed algorithm is verified by a tracking test using a micro aerial vehicle (MAV). The remainder of this paper is organized as follows. Section 2 describes the procedure for determining an adaptive SR, the structure of the FCNN for tracking, and the scale and coordinate conversions needed for the SR-based tracking approach. The data used in this study are introduced in Section 3. Section 4 presents the tracking results in terms of accuracy, robustness, and computational speed of the proposed algorithm. Finally, Section 5 summarizes the study and concludes the paper.