# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 2 Related Work  
At present, the gesture-based robot control method is widely used in daily life, industrial production and other fields because of its high ease of use, high recognition accuracy, and low learning cost. In [16], a gesture-based indoor mobile robot was proposed, and the movement of the robot was controlled by gestures captured by a Kinect camera. In order to improve the robot's gesture recognition ability, [17] integrated the features of the data obtained from the visual system and the inertial system, and evaluated the influence of four commonly used algorithms on gesture recognition. The gesture-based human-machine interaction was introduced in detail in [18], but the methods involved are mostly applicable to the air environment. In the underwater environment, the robustness and repeatability of these methods are questionable due to the influence of color distortion, low light, bubble occlusion, and other problems [6].  
In [19], sufficient research has been made on gesture interaction in the underwater environment. Considering the influence caused by complex scenes, divers' clothes, and harsh lighting conditions, a more complex Caddian language was designed to complete the communication between divers and AUVs. This language is built upon consolidated and standardized underwater gestures, which form control commands through different gestures to accomplish complex tasks such as ascending a fixed distance or taking photos at fixed points. While it is possible to enhance underwater gestures to communicate information, because of not considering the actual physical problems in the implementation, the practicality and understandability of gestures is not high.  
In recent years, in view of the strong recognition performance of neural networks, many studies [20; 21; 22] have adopted convolutional neural networks to complete underwater human-machine interaction based on gestures, achieving good recognition and control effects. However, in terms of experimental implementation, the above schemes are all based on the test data in the laboratory, without combining with the actual hardware equipment to obtain the experimental verification results in the real scene.  
For the choice of high-performance mobile devices, embedded AI devices such as NVIDIA Jetson series are widely used. The reason is not only that Jetson can provide powerful AI computing power and parallel computing function, but also that it has the characteristics of a small size, low power consumption and rich external expansion. At the same time, the Jetson platform is compatible with the jetpack software development kit (SDK), so that it can run applications such as deep learning, computer vision, and accelerated computing software libraries. These features enable Jetson to be used in various scenarios, including auto driving [23; 24; 25], industrial quality monitoring [26], medical testing [27; 28], etc. [29; 30; 31].  
Less research work has been done on human-machine gesture interaction underwater. The related works of underwater robot systems based on gesture recognition and assisted by target tracking are very few and there are many aspects yet to be examined.