Cross-scene crowd counting via deep convolutional neural networks

Cross-scene crowd counting is a challenging task where no laborious data annotation is required for counting people in new target surveillance crowd scenes unseen in the training set. The performance of most existing crowd counting methods drops significantly when they are applied to an unseen scene. To address this problem, we propose a deep convolutional neural network (CNN) for crowd counting, and it is trained alternatively with two related learning objectives, crowd density and crowd count. This proposed switchable learning approach is able to obtain better local optimum for both objectives. To handle an unseen target crowd scene, we present a data-driven method to fine-tune the trained CNN model for the…

• NN
  National Natural Science Foundation of China

  Awards: B07022, 61221001, 61129001, 61025005, 61301269
• SA
  Science and Technology Commission of Shanghai Municipality

  Awards: 61025005, 61221001, B07022, 14XD1402100, 13511504501
• CU
  Chinese University of Hong Kong