Key points are not available for this paper at this time.
This project aims to develop an innovative system for accurately tracking employees' working hours based on their presence within designated workspace areas, particularly their work cabins. Leveraging advanced technologies such as image annotation, preprocessing, and augmentation, as well as robust object detection models, this study addresses the need for efficient employee surveillance and time management solutions in contemporary workplaces. The methodology involved detailed annotation and enhancement of image data, enabling precise representation of cabin areas and identification of individual employees within images. Subsequently, a state-of-the-art object detection model, YOLOv8, was utilized to train using this annotated dataset, achieving an impressive accuracy of more than 90% in recognizing and tracking employee presence within the specified cabin regions. Through small incremental changes based on previous insights and optimization, the project achieved high levels of accuracy in inferring employees' working hours based on their occupancy within the designated workspace. By differentiating between time spent inside cabins (considered as working time) and time spent outside these areas (considered as non-working time), the system offers an automated and objective approach to time tracking, eliminating the need for manual input or subjective assessments. Future scopes for this research include exploring the integration of additional sensors or data sources to further enhance the accuracy and granularity of employee activity tracking. Additionally, advancements in machine learning algorithms and hardware may enable real-time processing and analysis of surveillance data, leading to more proactive management of employee productivity and well-being. Moreover, the application of this technology could extend beyond traditional office settings to various industries, such as manufacturing, retail, and healthcare, where precise monitoring of employee activities is crucial for optimizing operations and ensuring compliance with regulations. This research underscores the potential of advanced computer vision techniques, particularly YOLOv8, in revolutionizing employee surveillance and time management practices. By providing real-time monitoring capabilities and ensuring compliance with work regulations, this approach holds promise for enhancing workplace productivity, transparency, and accountability.
Das et al. (Tue,) studied this question.