Catheterization Laboratory (Cath Lab) is a hospital examination room equipped with diagnostic imaging equipment to display the heart arteries and to detect and treat any abnormality or stenosis. This thesis addresses the urgent need to improve the efficiency of operating rooms in Cath Labs due to the increasing number of patients with cardiovascular disease. Currently, measuring key performance indicators (KPI) for efficiency in Cath Labs requires manual observation and registration of patient entry and exit times, as well as the establishment and closure of access points. This process is time-consuming, costly, and prone to errors. To overcome these challenges, we conducted a study at the Reinier de Graaf Groep Hospital in Delft, where we recorded activities in the Cath Lab and developed a video-based event detection framework to automate the identification of these crucial events. The proposed framework consist of a fine-tuned YOLO v8 object detector, a popular object tracker ByteTrack, a post processing step and different event detection algorithms for different events of interest. This thesis presents the related video-based event detection framework and a detailed introduction for the development of object detector YOLO and object tracking technologies. The materials used in the project and how they work are clearly described. Events of interest are determined as needed for KPI calculations and are defined by describing the workflow in the target Cath lab. The steps taken to prepare the event dataset are also described in detail. The process of training, the connection between the tracker and the detector and the algorithms to detect different events for reproduction are explained. A series of performance tests on the target detector and tracker concluded that the tracker can handle the occlusion to some extent, but the performance of the object detector determined the performance of the tracker. It was also found that the simpler network structure in YOLOv8 outperformed the more complex network structure in detecting smaller objects on our dataset. In addition, the event detection algorithm is tested on multiple videos to evaluate its robustness and result showed that the algorithm can correctly and accurately detect the target events. The practical applicability of the framework and some future work that could enhance its performance are discussed. In conclusion, this thesis presents a video-based event detection framework that can detect events of interest in the Catheterization Lab at Delft Reinier de Graaf Groep Hospital.