## 1 Introduction  
A ship's bridge, serving as the command center, is a critical workspace, equipped with various navigation and communication tools, such as radars, a Global Positioning System (GPS), electronic chart systems, an Automatic Identification System (AIS), a Very High Frequency (VHF) radio communication system, and more. Multiple ship drivers are typically required to operate the ship and process the large amount of information coming from diverse equipment. Ship crew members who violate maritime transportation regulations face severe consequences, including as a result of causing serious injuries, substantial property damage, significant water pollution, and other adverse effects. For instance, on 6 August 2017, a recreational motor cruiser, James 2, and a commercial fishing vessel, Vertrouwen, collided in Sussex Bay, southeast of Shoreham harbor, resulting in James 2 sinking and three of its four crew members drowning, due to ineffective lookouts, a lack of lifejackets, improper navigation lights, and the watchkeeper being distracted by social media and vessel administration [1]. This incident underscores the necessity of the MCA's MGN 638 [2], which provides essential guidance on maintaining proper lookouts and avoiding distractions to enhance maritime safety. Therefore, it is crucial to enhance ship safety by developing an autonomous recognition system for monitoring crew behavior. While driver behavior identification in automobiles has made significant progress [3], similar technologies have found limited application within the domain of ship navigation. Compared to automobiles, the environment within a ship's bridge is more complex. Firstly, the ship's bridge is notably spacious. In contrast to a car, which typically has only one driver, a ship requires multiple crew members to operate the vessel together. Furthermore, automobile drivers are generally restricted to a seated position when driving. Ship crew may exhibit various postures, such as walking, sitting, or standing, with a significantly broader range of operational areas. Additionally, due to issues related to lighting or occlusion, the accuracy in detecting small objects, such as cigarettes or mobile phones, tends to be lower on a ship. Many algorithms perform excellently in scenarios with well-defined boundaries [4]; however, their efficacy diminishes significantly within the complex environment of a ship's bridge. The challenges faced in this environment increase the requirements for developing and optimizing behavior recognition algorithms. Based on the input, behavior recognition algorithms can be classified into two categories: single-frame recognition and multi-frame recognition. Single-frame recognition algorithms attempt to identify actions from a single image. Object detection algorithms can help determine whether a crew member is smoking or playing on a phone by detecting cigarettes, mobile phones, or other items in the crew member's hand. However, these objects are often small and have ambiguous features, leading to higher chances of false detection, especially in complex environments, like a ship's bridge. Consequently, object detection algorithms may produce unsatisfactory results, due to their limitations in regard to handling such ambiguity. In contrast, multi-frame behavior recognition algorithms process multiple consecutive frames simultaneously. By analyzing the sequences of frames, these algorithms can effectively extract motion features, such as the act of raising a hand while smoking or turning the steering wheel while driving, along with other temporal dynamics. This capability significantly enhances the feature extraction performance, providing more robust and accurate behavior identification over time. Moreover, the collection and analysis of high-quality data are crucial for maximizing model performance. The use of high-quality data not only enhance the training process, but also ensure that the model can generalize well to new, unseen scenarios, thereby substantially increasing its recognition accuracy.