Improving detector performance by learning from compressed samples

Increasing data volumes coupled with bandwidth limitations in on-board data transmission paths make data compression of automotive video signals indispensable. Since traditional image compression algorithms are solely tuned for optimal human perception, this work studies their effect on a nighttime automotive pedestrian detection system. Evaluating raw-data trained detectors on compressed video streams reveals detection rate declines for strong compression factors. On the other hand, when using image compression as training data preprocessing tool an increase in detection performance can be achieved.