{"references": ["1. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, (2015), Rich feature hierarchies for accurateobject detection and semantic segmentation http://arxiv.org/abs/1311.2524,doi:arXiv:1 311.2524.", "2.\tRen, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian,2015, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. https://doi.org/10.48550/arxiv.1 506.01497, doi:10.48550/ARXIV.1506.01497I.", "3.\tCao, Danyang et al. \"An improved object detection algorithm based on multi-scaledand deformableconvolutional neural networks.\" Humancentric Computing and Information Sciences 10 (2020): 1-m 22.DOI:10.1186/s13673-020-00219-9", "4.\tJ. Redmon, S. Divvala, R. Girshick and A. Farhadi, \"You Only Look Once: Unified,Real-Time ObjectDetection,\" 2016 IEEE Conference on Computer Vision and pattern Recognition (CVPR), 2016, pp. 779- 788,doi: 10.1109/CVPR.2016.91.", "5.\tWang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark (2022), YOLOv7: Trainable bag-of- freebies sets new state-of the-art for real- time object detectors, doi : 10.48550", "6.\tWei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, ScottE. Reed, Cheng-Yang Fu, &Alexander C. Berg (2015).\" SSD: Single Shot MultiBox Detector\". CoRR, abs/1512.02325.", "7.\tKrizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In F. Pereira, C. J. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems (Vol. 25). Retrieved From https://proceedings.neurips.cc/paper/201 2/file/c399862d3b9d6b76c846e924a68c45b-Paper.pdf", "8.\tSimonyan, Karen and Zisserman, Andrew,2014,\"Very Deep Convolutional Networks for Large-Scale Image Recognition\", arXiv,10.48550/ARXIV.1409.1556,https://arxiv.org/abs/1409.1556", "9.\tHe, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. CoRR, abs/1512.03385. Retrieved from http://arxiv.org/abs/1512.03385", "10.\tLin, Min and Chen, Qiang and Yan,Shuicheng, \"Network In Network\",arXiv(2013),10.48550/ARXIV.1312.4400, Retrieved from https://arxiv.org/abs/1312.4400."]}

Object Detection is a core computer-vision technique that detects the presence and location of an object in an image or in a sequence of images (video). Once an instance has been detected, it assigns a unique identification to it. It also has the ability to derive further information complimenting the object. Object Tracking is a machine learning technique that is highly sought after in the industrial sector to automate most of their processes and thus reduce labor. Object detection techniques have been developed rapidly for many different applications and these detection techniques can be implemented in a super-market environment to avoid the negatives of a traditional shopping experience. Our proposed system is an advanced modular shopping infrastructure that provides Stores with a frictionless shopping experience.