Weakly supervised pedestrian detector training by unsupervised prior learning and cue fusion in videos
The growth in the amount of collected video data in the past decade necessitates automated video analysis for which pedestrian detection plays a key role. Training a pedestrian detector using supervised machine learning requires tedious manual annotation of pedestrians in the form of precise bounding boxes. In this paper, we propose a novel weakly supervised algorithm to train a pedestrian detector that only requires annotations of estimated centers of pedestrians instead of bounding boxes. Our algorithm makes use of a pedestrian prior learnt in an unsupervised way from the video and this prior is fused with the given weak supervision information in a principled manner. We show on publicly available datasets that our weakly supervised algorithm reduces the cost of manual annotation by over 4 times while achieving similar performance to a pedestrian detector trained with bounding box annotations.