Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model
This addresses the problem of reducing annotation costs for pedestrian detection in specific scenes, though it is incremental as it builds on existing latent models.
The paper tackles scene-specific pedestrian detection without human annotation by proposing a self-learning approach with a progressive latent model, achieving performance comparable to fully supervised methods and outperforming weakly supervised approaches.
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human' annotation involved. The self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedure, object locations in each frame are treated as latent variables that are solved with a progressive latent model (PLM). Compared with conventional latent models, the proposed PLM incorporates a spatial regularization term to reduce ambiguities in object proposals and to enforce object localization, and also a graph-based label propagation to discover harder instances in adjacent frames. With the difference of convex (DC) objective functions, PLM can be efficiently optimized with a concave-convex programming and thus guaranteeing the stability of self-learning. Extensive experiments demonstrate that even without annotation the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer learning and fully supervised approaches.