Abstract:

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.

Key words: transfer learning, Boosting, pedestrian detection, classification consensus