# Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model
## 1 Introduction  
Human unsafe behavior recognition stands at the crossroads of safety management and artificial intelligence applications, playing an indispensable role in preempting criminal activities and fortifying accident responses [1; 2; 3]. This state-of-the-art technology hinges on computer-assisted video analyses, pinpointing and classifying specific human actions. Yet, the effectiveness of these systems is, more often than not, impeded by challenges like occlusions, motion perspective shifts, and intricate backgrounds, all of which complicate exhaustive information extraction [4; 5; 6].  
The construction industry, infamous for its high incidence of safety mishaps attributed to human behaviors, presents a pertinent case study [7; 8; 9]. Traditional safety oversight, largely predicated on managerial surveillance, exhibits glaring inefficiencies, exacerbated by transient personnel, rapidly evolving environments, and convoluted production dynamics [10]. Even with the exponential growth of data-driven insights, hurdles such as integration bottlenecks and elevated overheads persist, necessitating an innovative shift in construction safety paradigms.  
Amidst this backdrop, intertwining safety management with computational methodologies is emerging as a promising research direction. The recent renaissance in deep learning equips us with sophisticated object detection frameworks--algorithms like R-CNN,Fast R-CNN, and YOLO being prime examples [11; 12]. These pioneering technologies herald a transformative era in safety management, streamlining data acquisition, processing, and dissemination.  
The scholarly community has made remarkable headway in worker safety helmet detection, leveraging cutting-edge algorithms. [PERSON] et al. [13] laid the groundwork, birthing the inaugural algorithm tailored for automatic worker safety helmet identification. Subsequent trailblazers like [PERSON] et al. [14] and [PERSON] et al. [15] further refined detection accuracy. Delving into nuances, [PERSON] et al. [16] and [PERSON] [17] extended detection capabilities, differentiating helmet nuances and optimizing detection metrics. In the realm of ear print recognition, [PERSON] et al. [18] introduced MDFNet, a lightweight unsupervised network known for its efficiency. Utilizing gradient magnitude, direction, and data-driven filters, MDFNet outperformed many existing methods, showing high recognition rates and robustness against occlusions on three public datasets. Despite such advancements, glaring gaps persist. Contemporary detection methodologies often neglect intricate spatiotemporal dynamics ingrained in human motions. Human action recognition is inherently nuanced, necessitating the discernment of both distinct action types and subtle variations within identical actions. The predominant models, particularly spatiotemporal two-stream convolution, falter when faced with intricate or ambiguous human behaviors.  
While these strides in helmet detection technology are commendable, the broader domain of human behavior detection still grapples with challenges. Most existing methodologies tend to overlook the complex spatiotemporal dynamics inherent to human movements. The intricacies of human action recognition are profound, given its nature of interclass recognition; not only do we have to categorize distinct types of actions, but there is also the nuance of identifying subtle variations within the same action, often influenced by its amplitude or intensity. Conventional models, especially spatiotemporal two-stream convolution, find themselves outpaced in scenarios demanding the recognition of complex or ambiguous human actions. Recognizing these gaps, this paper ventures to introduce an optimized two-stream convolution model, meticulously crafted for pinpointing unsafe behavior.