IEEE Trans Image Process. 2018 Mar;27(3):1418-1432. doi: 10.1109/TIP.2017.2779271. Epub 2017 Dec 4.
The difference between sample distributions of public data sets and specific scenes can be very significant. As a result, the deployment of generic human detectors in real-world scenes most often leads to sub-optimal detection performance. To avoid the labor-intensive task of manual annotations, we propose a semi-supervised approach for training deep convolutional networks on partially labeled data. To exploit a large amount of unlabeled target data, the knowledge learnt from public data sets is transferred to new model training by adapting an auxiliary detector to the target scene. We hypothesize that the components of the auxiliary detector capture essential human characteristics useful for constructing a scene-adapted detector. A selective ensemble algorithm is proposed to select a subset of the components relevant to the target scene for recombination. The resulting model is applied for collecting high-confidence samples from unlabeled target data. Furthermore, a deep convolutional network is trained by progressively labeling and selecting new training samples in a self-paced way. The detailed experimental evaluation verifies the effectiveness and superiority of the proposed approach in scene-specific human detection.
公共数据集和特定场景之间的样本分布差异可能非常显著。因此，通用人体探测器在实际场景中的部署通常会导致检测性能不佳。为了避免手动注释的繁重工作，我们提出了一种半监督方法，用于在部分标记数据上训练深度卷积网络。为了利用大量未标记的目标数据，通过自适应辅助检测器到目标场景，从公共数据集学习的知识被转移到新的模型训练中。我们假设辅助检测器的组件捕获了对构建场景自适应检测器有用的基本人体特征。提出了一种选择性集成算法来选择与目标场景相关的组件子集进行重组。将得到的模型应用于从未标记的目标数据中收集高置信度样本。此外，通过逐步标记和以自定进度选择新的训练样本，对深度卷积网络进行训练。详细的实验评估验证了所提出的方法在特定场景下人体检测中的有效性和优越性。
