JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2013, Vol. 43 ›› Issue (4): 26-31.

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Transfer learning model based on classification consensus and  its application in pedestrian detection

YU Li-ping1,2, TANG Huan-ling1,2   

  1. 1. School of Computer Science and Technology, Shandong Institute of Business and Technology, Yantai 264005, China;
    2. Key Laboratory of Intelligent Information Processing in Universities of Shandong (Shandong Institute of Business and Technology), Yantai 264005, China
  • Received:2013-05-14 Online:2013-08-20 Published:2013-05-14

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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

CLC Number: 

  • TP181
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