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Locate, size, and count: Accurately resolving people in dense crowds via detection

Sam, DB and Peri, SV and Sundararaman, MN and Kamath, A and Babu, RV (2021) Locate, size, and count: Accurately resolving people in dense crowds via detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (8). pp. 2739-2751.

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Official URL: https://doi.org/10.1109/TPAMI.2020.2974830


We introduce a detection framework for dense crowd counting and eliminate the need for the prevalent density regression paradigm. Typical counting models predict crowd density for an image as opposed to detecting every person. These regression methods, in general, fail to localize persons accurate enough for most applications other than counting. Hence, we adopt an architecture that locates every person in the crowd, sizes the spotted heads with bounding box and then counts them. Compared to normal object or face detectors, there exist certain unique challenges in designing such a detection system. Some of them are direct consequences of the huge diversity in dense crowds along with the need to predict boxes contiguously. We solve these issues and develop our LSC-CNN model, which can reliably detect heads of people across sparse to dense crowds. LSC-CNN employs a multi-column architecture with top-down feature modulation to better resolve persons and produce refined predictions at multiple resolutions. Interestingly, the proposed training regime requires only point head annotation, but can estimate approximate size information of heads. We show that LSC-CNN not only has superior localization than existing density regressors, but outperforms in counting as well. The code for our approach is available at https://github.com/val-iisc/lsc-cnn. © 1979-2012 IEEE.

Item Type: Journal Article
Publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society
Keywords: Forecasting; Regression analysis, Counting models; Crowd density; Detection framework; Detection system; Face detector; Head annotations; Multiple resolutions; Regression method, Object detection, adult; article; human; prediction
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 20 Nov 2021 08:46
Last Modified: 22 Nov 2021 09:22
URI: http://eprints.iisc.ac.in/id/eprint/69833

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