Selective Ensemble Network for Accurate Crowd Density Estimation

Abstract

This paper proposes a selective ensemble deep network architecture for crowd density estimation and people counting. In contrast to existing deep network-based methods, the proposed method incorporates two sub-networks for local density estimation: one to learn sparse density regions and one to learn dense density regions. Locally estimated density maps from the two sub-networks are selectively combined in ensemble fashion using a gating network to estimate an initial crowd density map. The initial density map is refined as a high resolution map, using another sub-network that draws on contextual information in the image. In training, a novel adaptive loss scheme is applied to resolve an ambiguity in the crowded region. The proposed scheme improves both density map accuracy and counting accuracy by adjusting the weighting value between density loss and counting loss according to the degree of crowdness and training epochs. Experiments using public datasets confirm that the proposed method outperforms state-of-the-art methods. Through self-evaluation, the effectiveness of each part in the network is also verified.