Autonomous in situ training of classification modules in real-time vision systems and its application to pedestrian recognition

摘要

This contribution describes a classification module integrated into an image processing system consisting of a subsequent detection, tracking, and classification stage that extends its "knowledge" about a certain object class by autonomous in situ training. in our scenario, a classifier initially trained with front and rear views of pedestrians mainly in dark clothes subsequently extends its recognition capabilities in a first step towards lateral views of pedestrians in dark clothes, in a second step towards lateral views of pedestrians wearing both dark and bright clothes.