  A continual learning solution is proposed to address the out-of-distribution
generalization problem for pedestrian detection. While recent pedestrian
detection models have achieved impressive performance on various datasets, they
remain sensitive to shifts in the distribution of the inference data. Our
method adopts and modifies Elastic Weight Consolidation to a backbone object
detection network, in order to penalize the changes in the model weights based
on their importance towards the initially learned task. We show that when
trained with one dataset and fine-tuned on another, our solution learns the new
distribution and maintains its performance on the previous one, avoiding
catastrophic forgetting. We use two popular datasets, CrowdHuman and
CityPersons for our cross-dataset experiments, and show considerable
improvements over standard fine-tuning, with a 9% and 18% miss rate percent
reduction improvement in the CrowdHuman and CityPersons datasets, respectively.
