Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings

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Multiscale adaptive object detection of Customer Behavior Analysis with MAdCo approach
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Published: Jun 11, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i6s.6938
Keywords:
Object detection, customer behavior, Multiscale, Adaptive, Contrastive feature learning, Discriminative features, Scale invariance, Viewpoint invariance, Benchmark datasets

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Priyanka Kaushik
Professor ,Computer Science Engineering Dept (AIT CSE ( AIML)) Chandigarh University Punjab
Saurabh Pratap Singh Rathore
Director, International Consortium of Academic Professionals for Scientific Research
Pramneet Kaur
Research Scholar Sri Venkateshwara university
Harsh Kumar
CSE Dept (AIT CSE ( AIML)) Chandigarh University
Nancy Tyagi
CSE Dept (AIT CSE ( AIML)) Chandigarh University

Abstract

Multiscale adaptive object detection is a powerful computer vision technique that holds great potential for customer behavior analysis in various domains. By accurately detecting and tracking objects of interest, such as customers or products, at different scales, this approach enables detailed analysis of customer behavior. It allows businesses to track customer movements, interactions with products, and dwell times, providing valuable insights into shopping patterns and preferences. The application of multiscale adaptive object detection in customer behavior analysis offers businesses the opportunity to optimize store layouts, product placements, and marketing strategies, leading to enhanced customer experiences and improved business performance. In this paper, we introduce an innovative technique for object detection that leverages contrastive feature learning to augment the efficacy of multiscale object detection. Our methodology incorporates a contrastive loss function to extract discriminative features that exhibit resilience to scale and perspective disparities. This empowers our model to precisely detect objects across a broad range of sizes and viewpoints, even in arduous scenarios encompassing partial occlusion or low contrast against the background. Through comprehensive experiments conducted on benchmark datasets, we demonstrate that our approach surpasses state-of-the-art methodologies in terms of both accuracy and efficiency.

Article Details

How to Cite
Kaushik, P. ., Singh Rathore, S. P. ., Kaur, P. ., Kumar, H. ., & Tyagi, N. . (2023). Leveraging Multiscale Adaptive Object Detection and Contrastive Feature Learning for Customer Behavior Analysis in Retail Settings. International Journal on Recent and Innovation Trends in Computing and Communication, 11(6s), 326–343. https://doi.org/10.17762/ijritcc.v11i6s.6938
Issue
Vol. 11 No. 6s (2023): Advances in Computational Modeling and Simulation of Computing Systems
Section
Articles

References

Song, H., et al. (2018). Pyramid box: A context-assisted single shot object detector. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778).

Redmon, J., et al. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

Zhang, X., et al. (2020). CPL: Contrastive pre-training of convolutional neural networks for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14944-14953).

Tian, Y., et al. (2021). Cosine normalized representation learning for visual object tracking. IEEE Transactions on Image Processing, 30, 1838-1851.

Wang, Z., et al. (2021). Multiscale adaptive object detection with contrastive feature learning. arXiv preprint arXiv:2106.

Lowe, D. G. (1999). Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision (Vol. 2, pp. 1150-1157).

Lin, T. Y., et al. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117-2125).

Liu, W., et al. (2016). SSD: Single shot multibox detector. In Proceedings of the European Conference on Computer Vision (pp. 21-37).

Lin, T.Y., et al. (2018). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2980-2988).

T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Feature pyramid networks for object detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 2117-2125.

S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: towards real-time object detection with region proposal networks," IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149, 2017.

K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2980-2988.

T. Lin, P. Goyal, R. Girshick, and K. He, "Focal loss for dense object detection," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), 2017, pp. 2980-2988.

Z. Cai and N. Vasconcelos, "Cascade R-CNN: delving into high quality object detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2018, pp. 6154-6162.

H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, "Pyramid scene parsing network," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 6230-6239.

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The PASCAL Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, Jun. 2010.

T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, "Microsoft COCO: Common Objects in Context," in Proceedings of the European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sep. 2014, pp. 740-755.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, "PyTorch: An Imperative Style, High-Performance Deep Learning Library," in Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2019, pp. 8024-8035.

J. Dai, Y. Li, K. He, and J. Sun, "R-FCN: Object Detection via Region-based Fully Convolutional Networks," in Proceedings of the Neural Information Processing Systems (NIPS) Conference, Barcelona, Spain, Dec. 2016, pp. 379-387.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," in Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, Oct. 2016, pp. 21-37.

Zhou, X., Wang, D., & Krähenbühl, P. (2019). Objects as points. arXiv preprint arXiv:1904.07850.

Tian, Z., Shen, C., Chen, H., & He, T. (2019). FCOS: Fully convolutional one-stage object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9627-9636).

Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-NMS: Improving object detection with one line of code. In Proceedings of the IEEE International Conference on Computer Vision (pp. 5562-5570).

Rathore, R. (2022). A Study on Application of Stochastic Queuing Models for Control of Congestion and Crowding. International Journal for Global Academic & Scientific Research, 1(1), 1–6. https://doi.org/10.55938/ijgasr.v1i1.6

Sharma, V. (2022). A Study on Data Scaling Methods for Machine Learning. International Journal for Global Academic & Scientific Research, 1(1), 23–33. https://doi.org/10.55938/ijgasr.v1i1.4

Rathore, R. (2022). A Review on Study of application of queueing models in Hospital sector. International Journal for Global Academic & Scientific Research, 1(2), 1–6. https://doi.org/10.55938/ijgasr.v1i2.11

Kaushik, P. (2022). Role and Application of Artificial Intelligence in Business Analytics: A Critical Evaluation. International Journal for Global Academic & Scientific Research, 1(3), 01–11. https://doi.org/10.55938/ijgasr.v1i3.15

Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017). Soft-NMS: Improving object detection with one line of code. In Proceedings of the IEEE International Conference on Computer Vision (pp. 5562-5570).

Kaushik P., Deep Learning and Machine Learning to Diagnose Melanoma; International Journal of Research in Science and Technology, Jan-Mar 2023, Vol 13, Issue 1, 58-72, DOI: http://doi.org/10.37648/ijrst.v13i01.008

Amr El Koshiry , A. E. x, Eliwa, E. ., & Hameed, H. A. . (2023). The Effectiveness of Using Blockchain Technology in Building High-Quality Educational Content Based on A Participatory Learning Environment and Its Impact on Increasing Student Achievement. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 50–62. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2443.

Kaushik P., Enhanced Cloud Car Parking System Using ML and Advanced Neural Network; International Journal of Research in Science and Technology, Jan-Mar 2023, Vol 13, Issue 1, 73-86, DOI: http://doi.org/10.37648/ijrst.v13i01.009

Kaushik, P. (2023). Artificial Intelligence Accelerated Transformation in The Healthcare Industry. Amity Journal of Professional Practices, 3(01). https://doi.org/10.55054/ajpp.v3i01.630

Kaushik, P. (2023). Congestion Articulation Control Using Machine Learning Technique. Amity Journal of Professional Practices, 3(01). https://doi.org/10.55054/ajpp.v3i01.631

Rathore, R. (2023). A Study Of Bed Occupancy Management In The Healthcare System Using The M/M/C Queue And Probability. International Journal for Global Academic & Scientific Research, 2(1), 01–09. https://doi.org/10.55938/ijgasr.v2i1.36