Patent ID: 8812344

Claim:
A method for determining impact of crowding on retail performance based on a measurement for behavior patterns of people in a store area using computer vision-based behavior analysis and segmentation measurement, comprising the following steps of: a) processing a plurality of input images in order to track each person among the people using a computer, by applying a computer vision-based tracking algorithm to the plurality of input images that are captured by a means for capturing images in the store area, wherein the plurality of input images are transferred to the computer via a means for video interface, b) identifying a subset of the people as a crowd based on a first path analysis of tracks by tracking each person among the people, c) measuring the behavior patterns of a person based on a second path analysis of tracks by tracking the person in relation to the crowd, d) measuring segmentation of the person in relation to the crowd, e) aggregating the measurements for the behavior patterns and segmentation over a predefined window of time, using the computer, and f) calculating a crowd index and a crowd impact index for the store area based on the measurements, using the computer, g) measuring elasticity of behavior of the people with respect to crowding, wherein the elasticity is defined as a change in behavioral response, the elasticity changes, depending on season, occasion, time-of-day, or trip type, and the elasticity is measured per segment that includes a demographic group or a group of people with a same trip type, and h) calculating an average density of sections in the store area over a predefined period of time, wherein the density is measured based on traffic counts using the computer vision-based tracking of each person, wherein the first path analysis comprises an application of a proximity rule among the tracks, wherein the crowd impact index comprises a traffic count and a shopping time index of people outside the crowd and whose shopping activity is impacted by the crowd, wherein the segmentation includes classification of demographic groups and trip types of the people, and wherein the trip types include stock-up trip, fill-in trip, quick trip, and occasion-based trip.