Exploiting Target Data to Learn Deep Convolutional Networks for Scene-Adapted Human Detection.

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.