中文摘要：

利用迁移学习解决在特定场景下尤其是在摄像头静止的监控场景下的行人检测问题，提出基于分类一致性的学习模型。利用Boosting技术从辅助训练集中选择具有正迁移能力的样本，对样本迁移能力给出了基于辅助分类器分类一致性的熵度量方法。对比实验表明，该学习模型能够有效地提高检测率，尤其是在标记样本较少的情况下仍得到了较好的检测效果。

英文摘要：

Based on the classification consensus, a novel transfer learning model for a scene-specific pedestrian detector especially in video surveillance with stationary cameras was propose. According to boosting technology, the samples showed positive transferability in auxiliary data set were selected and added to the target data set. The entropy-based transferability measurement was derived from the consensus on the predictions of auxiliary classifications. Experimental results showed that the proposed approach could improve the detection rate, especially with the insufficient labeled data.