Moving Object Detection Based on Clustering and Event-Based Camera

The Moving object detection as a problem in computer vision, has the attention of researchers for its need of different applications. Event cameras are used to support moving object detection missions depending on the event camera efficiently capturing the events of moving objects compared to classic cameras. In this paper, we proposed a model that used a clustering algorithm as a machine learning approach to help detect moving objects that were captured using an event camera. The proposed model used Hierarchical clustering algorithm called “Agglomerative” and compared to partitioning and density-based clustering algorithms for the mission of detecting moving objects. Moreover, the model shows better results compared to others in previous studies with “92.07” F1-score as a performance measure.