Scale and density invariant head detection deep model for crowd counting in pedestrian crowds
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ORIGINAL ARTICLE
Scale and density invariant head detection deep model for crowd counting in pedestrian crowds Sultan Daud Khan1
· Saleh Basalamah1
Accepted: 4 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Crowd counting in high density crowds has significant importance in crowd safety and crowd management. Existing state-ofthe-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. Keywords Dense scales · Crowd counting · Head detection · High density crowds
1 Introduction For crowd safety and security, it is important to automatically understand high density crowd dynamics in a faster way. However, automated understanding of crowd dynamics is a challenging job. Several efforts have been done during recent years to overcome those challenges. To understand crowd dynamics, crowd counting has gained much attention from research community. Counting the number of people and estimating the distribution of people in the environment provide valuable information for crowd managers. Considerable amount of work is reported in literature on crowd counting in high density crowds. Most of the existing methods treat the crowd counting problem as regression problem that only estimate the crowd count and avoid localization of individuals in the scene. Pedestrian detection provides the exact location of individuals in the scene (in terms of bounding boxes), which on the one hand, provides crucial information for crowd dwellers
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Sultan Daud Khan [email protected] National University of Technology, Islamabad, Pakistan
and on the other hand, serve as useful input for other crowd applications, for example, tracking, behavior understanding and anomaly detection. Despite significant importance, limited amount of work is reported in literature to detect pedestrians in high density crowds. The task of pedestrian detection in high density crowds is extremely challenging due to severe cl
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