Cross-domain pedestrian detection via feature alignment and image quality assessment

Datasets collected under different sensors, viewpoints, or weather conditions cause different domains. Models trained on domain A applied to tasks of domain B result in low performance. To overcome the domain shift, we propose an unsupervised pedestrian detection method that utilizes CycleGAN to establish an intermediate domain and transform a large gap domain-shift problem into two feature alignment subtasks with small gaps. The intermediate domain trained with labels from domain A, after two rounds of feature alignment using adversarial learning, can facilitate effective detection in domain B. To further enhance the training quality of intermediate domain models, Image Quality Assessment (IQA) is incorporated. The experimental results evaluated on Citypersons, KITTI, and BDD100K show that MR of 24.58%, 33.66%, 28.27%, and 28.25% were achieved in four cross-domain scenarios. Compared with typical pedestrian detection models, our proposed method can better overcome the domain-shift problem and achieve competitive results.