Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-toend cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network. This enables the layers in the network to learn globally relevant discriminative features which aid in estimating highly refined density maps with lower count error. The joint training is performed in an end-toend fashion. Extensive experiments on highly challenging publicly available datasets show that the proposed method achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods. 978-1-5386-2939-0/17/$31.00 c 2017 IEEE
Sindagi and Patel REF , on the other hand, use a cascaded network of CNNs to jointly learn crowd count classification and density map estimation in densely crowded scenes.
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
Computer Science