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Pedestrian counting is a fundamental tool for understanding pedestrianpatterns and crowd flow analysis. Existing works (e.g., image-level pedestriancounting, crossline crowd counting et al.) either only focus on the image-levelcounting or are constrained to the manual annotation of lines. In this work, wepropose to conduct the pedestrian counting from a new perspective - VideoIndividual Counting (VIC), which counts the total number of individualpedestrians in the given video (a person is only counted once). Instead ofrelying on the Multiple Object Tracking (MOT) techniques, we propose to solvethe problem by decomposing all pedestrians into the initial pedestrians whoexisted in the first frame and the new pedestrians with separate identities ineach following frame. Then, an end-to-end Decomposition and Reasoning Network(DRNet) is designed to predict the initial pedestrian count with the densityestimation method and reason the new pedestrian's count of each frame with thedifferentiable optimal transport. Extensive experiments are conducted on twodatasets with congested pedestrians and diverse scenes, demonstrating theeffectiveness of our method over baselines with great superiority in countingthe individual pedestrians. Code: https://github.com/taohan10200/DRNet.

DR.VIC: Decomposition and Reasoning for Video Individual Counting