## 1 Introduction  
Following decades of efforts from both academic and industrial researchers, unmanned aerial vehicle (UAV) technology has made significant progress, and it has begun to play an increasingly important role in various fields, such as aerial photography, transportation and delivery, public surveillance [1], and modern agriculture. Consequently, the interaction between UAVs and humans has become more frequent, and a growing demand has emerged for additional smart human-UAV interaction and control [2]. That is because traditional human-UAV interaction is not easy, and requires training and concentration during the flight operation [3], which usually limits the application of UAVs to thoroughly trained and professional people [4]. To address this problem, numerous methods have been proposed, which can mainly be categorized as four types: wearable sensors [5], more user-friendly remote controllers (see Figure 1a,b), speech recognition [6], and gesture/action recognition [7; 8; 9]. Although the use of speech recognition appears to be the most efficient method for controlling the machine, and speech recognition has been applied in several language translation tasks, its performance will degrade significantly and become unreliable owing to factors such as background noise, and the wide variety of human voice accents and speaking speeds. Wearable sensors to control UAVs have also undergone rapid development, with the exoskeleton (for example, Flyjecket [10]), wearable glove [11], EMG sensor [5], and wearable watch (such as the EMPA watch) having been reported for application to human-UAV interaction (as shown in Figure 1). Although such methods are intuitive for the user, they usually require expensive devices and lack the ability to differentiate between an unconscious action and a similar pre-defined action. Compared to the traditional remote controller, recently developed user-friendly remote controllers (such as those depicted in Figure 2a,b) can aid a human beginner to fly a UAV with little training. The UAV pilot can easily control the UAV and complete the path planning using the touch-screen device. The smartphone-based controlling method may be considered as a variation of the traditional remote controller; however, with this method, the distance between the UAV and its pilot is usually less than 100 m, owing to the limitations of WiFi signal transmission [12]. In general, such methods are the most popular solutions and have been accepted by the community. However, in the case of a UAV rescue operation, as illustrated in Figure 2c, since the external pilot is usually far away from the person being rescued (hundreds of meters or even miles away), it will become time consuming and difficult for the operator to finish the rescue operation by using the remote controller for lacking some important information such as: wind direction and speed of rescue site and the 3D position relationship between the person to be rescued and the UAV platform. However, the person being rescued knows these information better than the pilot and the direct interaction between him and the UAV is considered as a better way to rescue person. On such consideration, the human-UAV interaction is far beyond flying the UAV. In such case, as the person needing rescue cannot access a remote controller, the UAV needs to understand the human action (or gesture) by itself and not through the UAV pilot (particularly in the case of tele-operation, where the time delay between the UAV and its pilot is critical). Compared with the remote controller, the gesture/action-recognition-based human-UAV interaction is the more natural and intuitive method that _\"...could improve flight efficiency, reduce errors, and allow users to shift their attention from the task of control to the evaluation of the information provided by  
Figure 1: Recently developed system for controlling a UAV in different ways. Figure 2: Human-UAV interaction is more than flying a UAV (like **(a,b)**.