Retail Traffic-Flow Analysis Using a Fast Multi-object Detection and Tracking System

被引：0

作者：

Cobos, Richard ^{[1
]}

Hernandez, Jefferson ^{[1
]}

Abad, Andres G. ^{[1
]}

机构：

[1] Escuela Super Politecn Litoral ESPOL, Ind Artificial Intelligence INARI Res Lab, Campus Gustavo Galindo Velasco 09-01-5863, Guayaquil, Ecuador

来源：

APPLICATIONS OF COMPUTATIONAL INTELLIGENCE, COLCACI 2019 | 2019年 / 1096卷

关键词：

Multi-object tracking; Shopping-carts detection; Indoor localization and tracking; Kalman filters; MULTITARGET;

D O I：

10.1007/978-3-030-36211-9_3

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Traffic-flow analysis allows to make critical decisions for retail operation management. Common approaches for traffic-flow analysis make use of hardware-based solutions, which have major drawbacks, such as high deployment and maintenance costs. In this work, we address this issue by proposing a Multiple-Object Tracking (MOT) system, following the tracking-by-detection paradigm, that leverages on an ensemble of detectors, each running every f frames. We further measured the performance of our model in the MOT16 Challenge and applied our algorithm to obtain heatmaps and paths for customers and shopping carts in a retail store from CCTV cameras.

引用

页码：29 / 39

页数：11

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