Unsupervised domain-adaptive scene-specific pedestrian detection for static video surveillance

Highlights：

• We propose a domain-adaptive video object detection framework to generate scene-specific pedestrian detectors in different scenarios without human-labeled target domain samples.

• We propose a cycle semantic transfer network to achieve adversarial domain adaptation across different surveillance videos.

• We propose an online gradual optimization algorithm to iteratively specialize a generic detector to a target domain. This algorithm exhibits better fault tolerance for mislabeled samples by simulating an active learning process.

• We conduct experiments on multiple datasets to evaluate the performance of the algorithm. The proposed self-learning method performs even better than supervised learning methods and existing scene-specific pedestrian detection methods.