Concept-aware ensemble system for pedestrian detection

For pedestrian detection in ADAS, using multiple classifiers generally performs better than using a single classifier in terms of accuracy since the classifiers can be made to complement one another. On the other hand, such a pedestrian detector needs to be tuned dynamically to the variation of real-world environment such as different poses of pedestrians and variable background. Thus the system is requested to incrementally accept new information while retaining the old one. This paper presents an environment-adaptive ensemble system that performs incremental learning for pedestrian detection. It combines a pedestrian detector comprised of multiple classifiers with a front-end concept recognizer that selectively turns on and off the member classifiers adaptively according to the recognized concept of the input image. It adopts an incremental learning algorithm to add a new classifier, which is trained with a newly added batch of dataset, to the existing ensemble. With the intervention of the front-end concept recognizer, the system can retain good accuracy for old environments while not losing the focus on current environment.