Design and Validation of a System for People Queue Statistics Estimation

Abstract

AbstractEstimating statistics of people queues is an important problem for many businesses. Monitoring statistics like average wait time, average service time and queue length help businesses enhance service efficiency, improve customer satisfaction and increase revenue. There is thus a need to design systems that can automatically monitor these statistics. Systems that use video content analytics on imagery acquired by surveillance cameras are ideally suited for such a monitoring task. This chapter presents the systematic design of a general solution for automated visual queue statistics estimation and its validation from surveillance video. Such a design involves the careful consideration of multiple variables such as queue geometry, service-counter type, illumination dynamics, camera viewpoints, people appearances etc. We address these variabilities via a suite of algorithms designed to work across a range of queuing scenarios. We discuss factors involved in the systematic validation of such a system such that realistic performance assessment over a wide range of operating conditions can be ensured.We address validation, evaluation parameters and deployment considerations for this system and demonstrate the performance of the proposed solution.KeywordsService TimePedestrian DetectionAverage Service TimePerson AppearanceCamera ViewpointThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.