# Visual MAV Tracker with Adaptive Search Region
## 1 Introduction  
Deep convolutional neural networks (CNNs) have significantly improved the accuracy of object recognition from images [1]. However, deep networks require a relatively long computation time, which can hinder real-time detection and tracking with the limited computational resources of embedded systems, such as field robots, autonomous drone controls, and wearable robots. Therefore, the computational costs of object recognition algorithms need to be reduced. Object recognition algorithms can be classified into object trackers and object detectors. An object detector is used to estimate the location, size, and class of objects from a single image. It requires both regression and classification. Regression is used to search the location and size of objects in images, and classification determines the type of detected object is. The most recent CNN-based models utilize bounding boxes to determine the location and size of an object [2, 3]. The deformable part model [4] involves a sliding window approach for object detection. However, this requires a long computational time for sliding the window throughout the entire image. Furthermore, several different windows are needed to accurately determine the sizes of the bounding boxes for matching individual objects. Region-based CNN (R-CNN) networks have also been proposed, which use region proposals to acquire regions of interest for target objects [5, 6, 7, 8, 9, 10]. An R-CNN is a two-stage detector; the first stage detects regions of interest in images, whereas the second stage determines the accurate location, size, and object class. This approach greatly improved the mean average precision of detections, but still required a long computational time. Subsequently, faster R-CNN [8] was introduced, which reduced the computation time to approximately 5 FPS, which is still not sufficient for real-time detection. Single-stage detectors (e.g., YOLO(you only look ones) [11, 12, 13] and SSD(single shot detection) [14])have been developed to address this issue. These detectors decrease the computational time substantially and can be used for real-time detection. However, these models are less accurate than a dual-stage detector, particularly when the size of the target objects in the image is extremely large or extremely small. Most of the recent models deployed pre-trained backbone networks such as VGG-net [15], GoogLeNet [16], and ResNet [3] for feature extraction. In addition, an optimized anchor box was used to achieve robustness against different target object shapes [17]. A feature pyramid network was also introduced to accurately detect objects of different scales and sizes [18]. However, if these features are added to the algorithm, the computational cost of the detectors increases. Furthermore, the performance of most detectors is limited with respect to tracking a moving object because they do not use temporal information. Visual object tracking refers to the task of predicting the location of a target object in subsequent images using current and past information [19]. Tracking algorithms can be classified into correlation filter models, CNN trackers with partial online learning, and CNN trackers without online learning [20; 21]. Correlation filter models extract features from search windows. Subsequently, filters are obtained such that the convolution of the filters and the target region produces a Gaussian-shaped map centered on the midpoint of the target. The filters are continuously updated online to adapt to changes in the rotation, scale, and color across several frames. Various numerical filters were created based on variance minimization [22], average minimization [23], squared error minimization [24; 25], and kernelized correlation filters [24; 25]. CNN filters were applied in a previous study [26] because CNNs exhibit high performance in terms of feature extraction. The online learning speed of this model was improved by applying a factorized convolutional operator, a compact generative model, and a sparse online updating scheme [27].