Ensemble-based learning using few training samples for video surveillance scenarios

The article targets the task of content-based multiple-instance people retrieval from video surveillance footage. This task is particularly challenging when applied on such datasets as the available samples to train the decisioning system and formulate the query are insufficient (one image, few frames, or seconds of video recording). To cope with these challenges we investigate three established ensemble-based learning techniques, e.g., boosting, bagging and blending (stacking). Such methods are based on a set of procedures employed to train multiple learning algorithms and combine their outputs, while functioning together as a unified system of decision making. The approach was evaluated on two standard datasets (accounting for 16 people searching scenario on ca. 53000 labeled frames). Performance in terms of F2-Score attained promising results while dealing with our current task.