The performance of offline learned pedestrian detectors significantly drops when they are applied to video scenes of various camera views, occlusions, and background structures. Learning a detector for each video scene can avoid the performance drop but it requires repetitive human effort on data annotation. In this paper, a self-learning approach is proposed, toward specifying a pedestrian detector for each video scene without any human annotation involved. Object locations in video frames are treated as latent variables and a progressive latent model (PLM) is proposed to solve such latent variables. The PLM is deployed as components of object discovery, object enforcement, and label propagation, which are used to learn the object locations in a progressive manner. With the difference of convex (DC) objective functions, PLM is optimized by a concave-convex programming algorithm. With specified network branches and loss functions, PLM is integrated with deep feature learning and optimized in an end-to-end manner. From the perspectives of convex regularization and error rate estimation, detailed optimization analysis and learning stability analysis of the proposed PLM are provided. The extensive experiments demonstrate that even without annotation involved the proposed self-learning approach outperforms weakly supervised learning approaches, while achieving comparable performance with transfer learning approaches.

中文翻译：

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自学习场景特定行人检测器的渐进潜在模型

当离线学习行人检测器应用于各种摄像机视图、遮挡和背景结构的视频场景时，它们的性能显着下降。为每个视频场景学习一个检测器可以避免性能下降，但它需要在数据注释上重复人工。在本文中，提出了一种自学习方法，为每个视频场景指定一个行人检测器，而不涉及任何人工注释。视频帧中的对象位置被视为潜在变量，并且提出了渐进式潜在模型 (PLM) 来解决此类潜在变量。PLM 被部署为对象发现、对象实施和标签传播的组件，用于以渐进方式学习对象位置。随着凸（DC）目标函数的差异，PLM 通过凹凸编程算法进行了优化。通过指定的网络分支和损失函数，PLM 与深度特征学习相结合，并以端到端的方式进行优化。从凸正则化和错误率估计的角度，提供了所提出的PLM的详细优化分析和学习稳定性分析。大量实验表明，即使不涉及注释，所提出的自学习方法也优于弱监督学习方法，同时实现了与迁移学习方法相当的性能。提供了所提出的 PLM 的详细优化分析和学习稳定性分析。大量实验表明，即使不涉及注释，所提出的自学习方法也优于弱监督学习方法，同时实现了与迁移学习方法相当的性能。提供了所提出的 PLM 的详细优化分析和学习稳定性分析。大量实验表明，即使不涉及注释，所提出的自学习方法也优于弱监督学习方法，同时实现了与迁移学习方法相当的性能。