Abandoned-Cart-Vision: Abandoned Cart Detection Using a Deep Object Detection Approach in a Shopping Parking Space

Abstract

Nowadays, seeing a large number of shopping carts abandoned in the parking lot is a typical occurrence at every supermarket. After being used by customers who left their shopping carts in the parking lot and never returned. This study presents a technique for detecting abandoned carts in parking lots. The proposed identification of abandoned shopping carts in parking areas enables supermarket management to quickly respond to consumer requirements for shopping carts while also providing enough parking space for vehicles. In this study, the YOLOv3 model, a state-of-the-art deep transfer learning object identification method, is utilized to construct a shopping cart detection model. Upon the result of the study, the detection model has a training and validation accuracy of 92.17 % and 93.80 %, respectively, with an mAP value of 93.00 %, according to the study's findings. Because of its outstanding performance, the proposed model is suitable for video surveillance equipment. The system achieved a total testing accuracy of 100 %, with detection per frame accuracy ranging from 40.03 % to 65.03 %.