  We propose a Multi-Task Learning (MTL) paradigm based deep neural network
architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and
count estimation. Crowd count estimation is challenging due to the non-uniform
scale variations and the arbitrary perspective of an individual image. The
proposed model has two related tasks, with Crowd Density Estimation as the main
task and Crowd-Count Group Classification as the auxiliary task. The auxiliary
task helps in capturing the relevant scale-related information to improve the
performance of the main task. The main task model comprises two blocks: VGG-16
front-end for feature extraction and a dilated Convolutional Neural Network for
density map generation. The auxiliary task model shares the same front-end as
the main task, followed by a CNN classifier. Our proposed network achieves 5.8%
and 14.9% lower Mean Absolute Error (MAE) than the state-of-the-art methods on
ShanghaiTech dataset without using any data augmentation. Our model also
outperforms with 10.5% lower MAE on UCF_CC_50 dataset.
