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.