Self-learning Scene-specific Pedestrian Detectors
using a Progressive Latent Model
Abstract
In this paper, a self-learning approach is proposed
towards solving scene-specific pedestrian detection problem without any human’ annotation involved. The selflearning 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 concaveconvex 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.