Scale and density invariant head detection deep model for crowd counting in pedestrian crowds

Crowd counting in high density crowds has significant importance in crowd safety and crowd management. Existing state-of-the-art methods employ regression models to count the number of people in an image. However, regression models are blind and cannot localize the individuals in the scene. On the other hand, detection-based crowd counting in high density crowds is a challenging problem due to significant variations in scales, poses and appearances. The variations in poses and appearances can be handled through large capacity convolutional neural networks. However, the problem of scale lies in the heart of every detector and needs to be addressed for effective crowd counting. In this paper, we propose a end-to-end scale invariant head detection framework that can handle broad range of scales. We demonstrate that scale variations can be handled by modeling a set of specialized scale-specific convolutional neural networks with different receptive fields. These scale-specific detectors are combined into a single backbone network, where parameters of the network is optimized in end-to-end fashion. We evaluated our framework on challenging benchmark datasets, i.e., UCF-QNRF, UCSD. From experiment results, we demonstrate that proposed framework beats existing methods by a great margin.

Automated summary

In high density crowds, crowd counting is important for safety and management. Existing methods use regression models to count people, but they cannot localize individuals. Detection-based crowd counting faces challenges due to scale, pose, and appearance variations. A proposed framework addresses scale variations using specialized convolutional neural networks, showing significant improvement over existing methods on benchmark datasets like UCF-QNRF and UCSD.