Unveiling Semantic Structure - Event-Driven Framework for Video Analysis

Human memory is impressive but often struggles to remember and recall detailed information from various experiences. To address this limitation in the context of video semantic segmentation, we propose a new "Event driven framework". This framework works as a fully connected architecture that includes storage units for keeping data on individual video frames and relations to capture connections between them. It uses specialized read and write modules to make information retrieval and updates more efficient. This setup allows the system to use past experiences stored in the memory framework to perform semantic segmentation on new video frames. The network learns from each frame and uses this knowledge to understand and segment future frames, leading to better overall performance. The effectiveness of the Event driven framework for segmentation is evaluated using two key metrics: mean region similarity 80%, which measures the overall overlap between predicted and actual regions, and mean contour accuracy 85.9%, which assesses how well the predicted contours match the actual object boundaries. Together, these metrics provide a comprehensive view of the segmentation’s effectiveness, considering both the extent of overlap and the precision of boundary alignment.