Congested scene classification via efficient unsupervised feature learning and density estimation

• Assemble a new data set to serve as the platform for crowd analysis.
• Present an efficient feature learning and selection to generate robust features.
• Propose a novel density feature pooling to encode the density clue.

An unsupervised learning algorithm with density information considered is proposed for congested scene classification. Though many works have been proposed to address general scene classification during the past years, congested scene classification is not adequately studied yet. In this paper, an efficient unsupervised feature learning approach with density information encoded is proposed to solve this problem. Based on spherical k-means, a feature selection process is proposed to eliminate the learned noisy features. Then, local density information which better reflects the crowdedness of a scene is encoded by a novel feature pooling strategy. The proposed method is evaluated on the assembled congested scene data set and UIUC-sports data set, and intensive comparative experiments justify the effectiveness of the proposed approach.