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The rapid development in visual crowd analysis shows a trend to count peopleby positioning or even detecting, rather than simply summing a density map. Italso enlightens us back to the essence of the field, detection to count, whichcan give more abundant crowd information and has more practical applications.However, some recent work on crowd localization and detection has twolimitations: 1) The typical detection methods can not handle the dense crowdsand a large variation in scale; 2) The density map heuristic methods sufferfrom performance deficiency in position and box prediction, especially in highdensity or large-size crowds. In this paper, we devise a tailored baseline fordense crowds location, detection, and counting from a new perspective, named asLDC-Net for convenience, which has the following features: 1) A strong butminimalist paradigm to detect objects by only predicting a location map and asize map, which endows an ability to detect in a scene with any capacity ($0\sim 10,000+$ persons); 2) Excellent cross-scale ability in facing a largevariation, such as the head ranging in $0 \sim 100,000+$ pixels; 3) Achievesuperior performance in location and box prediction tasks, as well as acompetitive counting performance compared with the density-based methods.Finally, the source code and pre-trained models will be released.

LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds