# Detection and Tracking of Moving Pedestrians with a Small Unmanned Aerial Vehicle
## 1 Introduction  
Recently, the use of small/miniature unmanned aerial vehicles (SUAV) or drones has increased for a variety of applications. SUAVs range from micro air vehicles to man-portable UAVs, classified by their weight or size [1]. The SUAV is cost-effective for capturing aerial scenes. The camera can be easily built and manipulated in order to capture the scene of interest in a long distance, however, the computational resources of the drone are often limited to processing high-resolution video sequences in real time. Visual object detection has been studied with various methods [2]. Methods based on background subtraction or frame difference were studied in [3; 4; 5; 6]. Gaussian mixture modeling (GMM) was used to analyze the background and target regions [3; 4]. In [5], the background was subtracted under a Gaussian mixture assumption, followed by a morphological filter. Long-range moving objects were detected by a drone in [6]. Visual tracking is of intense interest, with the development of digital camera technology and image processing technology [7; 8; 9; 10; 11]. Various experimental studies were surveyed in [7]. A particle filter was utilized with background subtraction in [8]. Deep learning-based visual tracking was researched in [9]. In [10; 11], supervised learning and reinforcement learning were adopted for visual tracking, respectively. Vison-based target tracking with UAV was researched in [12; 13; 14; 15]. Closely located-object tracking was performed with feature matching and multi-inertial sensing data in [12]. Pedestrians were tracked by template-matching in [13]. Small animals were tracked with a freely moving camera in [14]. A moving ground target was tracked in dense obstacles areas with UAV in [15]. Tracking can be performed by means of a consecutive estimation of the target state, such as the position, velocity, and acceleration [16]. The Kalman filter is known to be optimal under independent Gaussian noise assumption in estimating the target's dynamic state in real time [17]. The interacting multi model (IMM) can handle multiple targets with different maneuverings, because it can switch thetarget's dynamics between multiple modes [18]. IMM was researched with an unscented Kalman filter (UKF) in [19]. The effect of the multi-modal approach on high maneuvering was emphasized in [20]. Another consideration for multiple target tracking is data association, a method to assign each measurement to either established targets, a new target, or a false alarm. The Bayesian data association approach, probabilistic data association (PDA), calculates the probabilities of association between the target and the measurement. It has been extended to joint probabilistic data association (JPDA) to handle multiple targets [21]. Another Bayesian approach, multiple hypotheses tracking (MHT) [22], requires hypothesis reduction techniques to reduce computational complexity, which increases exponentially. A non-Bayesian data association approach, \(N\)-dimension (frame) assignment, was developed in [23]. In this paper, we address the detection and tracking of multiple moving pedestrians by an SUAV or drone. Visual detection is performed through frame subtraction, with thresholding and dilation operation, and false alarm removal [24; 25]. Each frame is subtracted from a past frame, separated by a constant interval. Then, thresholding generates a binary image and dilation is applied to the binary image to produce candidate target regions. Finally, false target regions are removed, with the known size of the real object. The centroids of final region of interest (ROI) windows are considered \(x\) and \(y\) positions, which are fed to the next tracking stage as measurements. This detection approach does not require intense training process and heavy computational burdens. Therefore, this method is suitable for autonomous stand-alone aerial video surveillance systems with a drone, which have limited computational resources.