Adapting pedestrian detectors to new domains: a comprehensive review

Kyaw Kyaw Htike and David Hogg, “Adapting pedestrian detectors to new domains: a comprehensive review”, Engineering Applications of Artificial Intelligence, Vol. 50, pp. 142-158, Elsevier, 2016. DOI: 10.1016/j.engappai.2016.01.029. [ISI and Scopus-indexed journal; Impact factor = 2.368]

Abstract

Successful detection and localisation of pedestrians is an important goal in computer vision which is a core area in Artificial Intelligence. State-of-the-art pedestrian detectors proposed in literature have reached impressive performance on certain datasets. However, it has been pointed out that these detectors tend not to perform very well when applied to specific scenes that differ from the training datasets in some ways. Due to this, domain adaptation approaches have recently become popular in order to adapt existing detectors to new domains to improve the performance in those domains. There is a real need to review and analyse critically the state-of-the-art domain adaptation algorithms, especially in the area of object and pedestrian detection. In this paper, we survey the most relevant and important state-of-the-art results for domain adaptation for image and video data, with a particular focus on pedestrian detection. Related areas to domain adaptation are also included in our review and we make observations and draw conclusions from the representative papers and give practical recommendations on which methods should be preferred in different situations that practitioners may encounter in real-life.

Keywords

Domain adaptation
Pedestrian detection
Feature learning
Scene-specific detector
Transfer learning