Cross-Scene Counting Based on Domain Adaptation-Extreme Learning Machine

Cross-scene counting is difficult if only limited training samples are available in the new scene. In this paper, a cross-scene counting model is learned with information transferred from other scenes. Counting is achieved through regression, which maps the features of crowds to their counts. Hand-crafted features are extracted from segmented crowd foregrounds obtained through block robust principal component analysis. Samples of existing scenes (source domain) are adaptively transferred into the new scene (target domain) through domain adaptation. Then, a counting model based on domain adaptation-extreme learning machine (DA-ELM) is efficiently learned via iterative optimization with training samples of both domains. Quantitative analysis indicates that the DA-ELM can count the crowds of a new scene with only a half of the training samples compared with counting without domain adaptation. Contrastive evaluations based on three benchmarking data sets are implemented with several state-of-the-art domain adaptation approaches, including hand-crafted feature-based and deep neural network-based approaches. Results reveal the effectiveness of DA-ELM in transferring information through embedding domain adaptation into an ELM framework.