Almost Unsupervised Learning for Dense Crowd Counting

Authors

  • Deepak Babu Sam Indian Institute of Science
  • Neeraj N Sajjan Indian Institute of Science
  • Himanshu Maurya Indian Institute of Technology
  • R. Venkatesh Babu Indian Institute of Science

DOI:

https://doi.org/10.1609/aaai.v33i01.33018868

Abstract

We present an unsupervised learning method for dense crowd count estimation. Marred by large variability in appearance of people and extreme overlap in crowds, enumerating people proves to be a difficult task even for humans. This implies creating large-scale annotated crowd data is expensive and directly takes a toll on the performance of existing CNN based counting models on account of small datasets. Motivated by these challenges, we develop Grid Winner-Take-All (GWTA) autoencoder to learn several layers of useful filters from unlabeled crowd images. Our GWTA approach divides a convolution layer spatially into a grid of cells. Within each cell, only the maximally activated neuron is allowed to update the filter. Almost 99.9% of the parameters of the proposed model are trained without any labeled data while the rest 0.1% are tuned with supervision. The model achieves superior results compared to other unsupervised methods and stays reasonably close to the accuracy of supervised baseline. Furthermore, we present comparisons and analyses regarding the quality of learned features across various models.

Downloads

Published

2019-07-17

How to Cite

Sam, D. B., Sajjan, N. N., Maurya, H., & Babu, R. V. (2019). Almost Unsupervised Learning for Dense Crowd Counting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8868-8875. https://doi.org/10.1609/aaai.v33i01.33018868

Issue

Section

AAAI Technical Track: Vision