Person count localization in videos from noisy foreground and detections

This paper formulates and presents a solution to a new problem called person count localization. Given a video of a crowded scene, our goal is to output for each frame a set of: 1) Detections optimally covering both isolated individuals and cluttered groups of people; and 2) Counts of people inside these detections. This problem is a middle-ground between frame-level person counting, which does not localize counts, and person detection aimed at perfectly localizing people with count-one detections. Our problem formulation is important for a wide range of domains, where people appear frequently under severe occlusion within a crowd. As these crowds are often visually distinct from the rest of the scene, they can be viewed as “visual phrases” whose spatially tight localization and count assignment could facilitate higher-level video understanding. For count localization, we specify a novel framework of iterative error-driven revisions of a flow graph derived from noisy input of people detections and foreground segmentation. Each iteration creates and solves an integer program for count localization based on iterative revisions of the flow graph. The graph revisions are based on detected violations of basic integrity constraints. They in turn trigger learned modifications to the graph aimed at reducing noise in input features. For evaluation, we introduce a new metric that measures both count precision and localization of our approach on American football and pedestrian videos.