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We propose a Multi-Task Learning (MTL) paradigm based deep neural networkarchitecture, called MTCNet (Multi-Task Crowd Network) for crowd density andcount estimation. Crowd count estimation is challenging due to the non-uniformscale variations and the arbitrary perspective of an individual image. Theproposed model has two related tasks, with Crowd Density Estimation as the maintask and Crowd-Count Group Classification as the auxiliary task. The auxiliarytask helps in capturing the relevant scale-related information to improve theperformance of the main task. The main task model comprises two blocks: VGG-16front-end for feature extraction and a dilated Convolutional Neural Network fordensity map generation. The auxiliary task model shares the same front-end asthe 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 onShanghaiTech dataset without using any data augmentation. Our model alsooutperforms with 10.5% lower MAE on UCF_CC_50 dataset.

MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation