Crowd Detection and Counting from Images using MResnet
Sai NitishaVemuri1, Srinivas Kudipudi2
1Sai Nitisha Vemuri, Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
2Srinivas Kudipudi, Department of CSE, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada (Andhra Pradesh), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2026-2030 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7801068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Crowd Detection and counting is important for crowd control and monitoring in places like pilgrimages. Automatic crowd detection from images have several challenges. Different scale variations and viewpoints of images make it difficult for crowd detection models to generalize for broader data. Most of the existing approaches for crowd detection contains multiple columns for extracting multi-scale features. By using multiple columns through a deeper network can cause the layers to lose features as the layers get deeper. In this paper, a new Multi-Residual Network (MResnet) is proposed for crowd detection and counting. MResnet contains multiple three columns sub-networks with three receptive field variations. The advantages of the proposed network is that each sub-network has a specific receptive field for imbalanced distribution of human crowd in the image. Residual connections are utilized in each subnetwork for information propagation. The M Resnet is evaluated using the ShanghaiTech dataset. Extensive experiments have shown that our proposed network achieves lower count error and high spatial localization.
Keywords: Crowd Detection, Crowd Counting, Deep Learning, Shanghitech Dataset.
Scope of the Article: Deep Learning