# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 1 Introduction  
With the increase of population and economic development, human beings need to develop marine resources to meet the needs of economic development. In recent years, underwater robots have begun to play an increasingly important role in marine exploration and development. Underwater robots are mainly divided into remote operated vehicles (ROVs) and automatic underwater vehicles (AUVs) [1, 2]. ROVs can complete complex underwater tasks through the manipulation of personnel on shore, and are widely used in many fields such as underwater mining, hull cleaning, and pipeline monitoring [3, 4]. AUVs are mainly used in marine resource exploration, mineral resource development, and other fields due to their strong autonomy and large diving depth [5]. The research and application of underwater robots has become increasingly mature, and it has made remarkable achievements in the field of marine exploration. However, for some important scenes such as exploration under complex terrain, aquatic resource fishing, underwater archaeology, and marine biological census, divers are still the main tool. However, on the one hand, the marine environment is dangerous, and the real-time monitoring of the underwater environment cannot be realized due to the weakening of electromagnetic waves by water bodies. On the other hand, human diving depth is very limited and depends on relevant equipment to maintain necessary breathing. All of these factors threaten the life safety of divers. For years, researchers have continuously improved autonomous underwater vehicles (AUVs) to help divers complete underwater work. Effective communication between the diver and the AUV is critical in the human-machine co-diving environment. A goodinteraction mode can maximize the role of AUVs and promote human-machine cooperation to achieve underwater tasks [6]. During this period, underwater optical images have been widely used in underwater archaeology [7], seabed resource exploration [8], underwater environment detection [9], and other fields due to their advantage of intuitive detection targets, high imaging resolution and high information content. At present, the wireless communication mode dominated by acoustics is often used in underwater communication due to the substantial limitation of the cable communication mode underwater. However, underwater acoustic communication still has two major drawbacks: one is the dependence on expensive acoustic equipment, and the other is the narrow band width and low rate of underwater acoustic signal transmission [10]. Therefore, at present, in order to realize the communication and interaction between the diver and the robot, many researchers [11; 12; 13] adopted the method of recognizing and analyzing the captured diver's gesture to obtain the diver's command and intention. In this way, information can be effectively transmitted between man and machine, and expensive acoustic equipment can be avoided [14]. Due to the particularity of the underwater environment and the absorption of light by water bodies, there are often situations in the underwater gesture interaction, such as inability to correctly capture gestures and a poor effect of gesture recognition [15]. To solve the above problems, we added underwater image enhancement and human tracking before gesture recognition. Underwater image enhancement can carry out color correction and image restoration, and restore the real scene information of the underwater image. Underwater human tracking can not only realize the continuous tracking of divers by an AUV to ensure that dives always stay in the field of vision of the AUV, but also focus on finding preset gestures from human body instead of the whole image to improve the accuracy of gesture recognition. In addition, due to the work under underwater, AUVs have requirements on the weight and volume of the equipment they carry, so they cannot load high-power and large-volume desktop-level computing equipment.