17.1.4.2 Surveillance Systems, Applied to Retail Business, Shoppers, Shopping

Chapter Contents (Back)
Surveillance. Retail. Shoppers.

Video Mining,
2000
WWW Link. Vendor, Surveillance. Primary market is market analysis (i.e. tracking shoppers in a store to analyze shopping patterns).

Conrad, G.L.[Gary L.], Denenberg, B.A.[Byron A.], Kramerich, G.L.[George L.],
Video traffic monitor for retail establishments and the like,
US_Patent5,465,115, Nov 7, 1995
WWW Link. BibRef 9511

Popa, M.C.[Mirela C.], Rothkrantz, L.J.M.[Leon J.M.], Shan, C.F.[Cai-Feng], Gritti, T.[Tommaso], Wiggers, P.,
Semantic assessment of shopping behavior using trajectories, shopping related actions, and context information,
PRL(34), No. 7, 1 May 2013, pp. 809-819.
Elsevier DOI 1303
BibRef
Earlier: A1, A4, A2, A3, A5:
Detecting Customers' Buying Events on a Real-Life Database,
CAIP11(I: 17-25).
Springer DOI 1109
Shopping behavior; Semantic analysis; Trajectory analysis; Action recognition; Hidden Markov Models BibRef

Popa, M.C., Rothkrantz, L.J.M., Shan, C., Wiggers, P.,
Assessment of customers' level of interest,
ICIP12(41-44).
IEEE DOI 1302
BibRef

Popa, M.C., Rothkrantz, L.J.M., Wiggers, P., Shan, C.,
Shopping behavior recognition using a language modeling analogy,
PRL(34), No. 15, 2013, pp. 1879-1889.
Elsevier DOI 1309
Shopping behavior BibRef

Ahn, H.I.[Hyung-Il], Picard, R.W.,
Measuring Affective-Cognitive Experience and Predicting Market Success,
AffCom(5), No. 2, April 2014, pp. 173-186.
IEEE DOI 1411
cognition BibRef

Popa, M.C.[Mirela Carmia],
Multimodal Assessment of Shopping Behavior,
ELCVIA(14), No. 3, 2015, pp. xx-yy.
DOI Link 1601
Thesis summary. BibRef

Liu, S.[Song], Li, W.Q.[Wan-Qing], Davis, S.[Stephen], Ritz, C.[Christian], Tian, H.[Hongda],
Planogram Compliance Checking Based on Detection of Recurring Patterns,
MultMedMag(23), No. 2, April 2016, pp. 54-63.
IEEE DOI 1605
Companies. Retail store layout analysis. BibRef

Pereira, E.M.[Eduardo Marques], Cardoso, J.S.[Jaime S.], Morla, R.[Ricardo],
Long-range trajectories from global and local motion representations,
JVCIR(40, Part A), No. 1, 2016, pp. 265-287.
Elsevier DOI 1609
BibRef
Earlier:
Motion Flow Tracking in Unconstrained Videos for Retail Scenario,
IbPRIA13(340-349).
Springer DOI 1307
Long trajectories BibRef

Quintana, M., Menendez, J.M., Alvarez, F., Lopez, J.P.,
Improving retail efficiency through sensing technologies: A survey,
PRL(81), No. 1, 2016, pp. 3-10.
Elsevier DOI 1609
Survey, Retail. Intelligent retail BibRef

Merad, D.[Djamal], Aziz, K.E.[Kheir-Eddine], Iguernaissi, R.[Rabah], Fertil, B.[Bernard], Drap, P.[Pierre],
Tracking multiple persons under partial and global occlusions: Application to customers' behavior analysis,
PRL(81), No. 1, 2016, pp. 11-20.
Elsevier DOI 1609
Multiple-people tracking
See also People's Re-identification Across Multiple Non-overlapping Cameras by Local Discriminative Patch Matching. BibRef

Merad, D.[Djamal], Drap, P.[Pierre], Lufimpu-Luviya, Y.[Yannick], Iguernaissi, R.[Rabah], Fertil, B.[Bernard],
Purchase behavior analysis through gaze and gesture observation,
PRL(81), No. 1, 2016, pp. 21-29.
Elsevier DOI 1609
Purchase behavior BibRef

Sturari, M.[Mirco], Liciotti, D.[Daniele], Pierdicca, R.[Roberto], Frontoni, E.[Emanuele], Mancini, A.[Adriano], Contigiani, M.[Marco], Zingaretti, P.[Primo],
Robust and affordable retail customer profiling by vision and radio beacon sensor fusion,
PRL(81), No. 1, 2016, pp. 30-40.
Elsevier DOI 1609
Sensor fusion BibRef

Ananthanarayanan, G., Bahl, P., Bodík, P., Chintalapudi, K., Philipose, M., Ravindranath, L., Sinha, S.,
Real-Time Video Analytics: The Killer App for Edge Computing,
Computer(50), No. 10, 2017, pp. 58-67.
IEEE DOI 1710
cloud computing, software architecture, video signal processing, edge computing, geographically distributed architecture, real-time video analytics, Automobiles, Bandwidth, Cameras, Cloud computing, Streaming media, Surveillance, Video analytics, BibRef

Ji, C.B.[Cui-Bin], Duan, G.J.[Gui-Jiang], Ma, H.Y.[Han-Yong], Zhang, L.[Long], Xu, H.Y.[Huan-Yun],
Modeling of image, video and text fusion quality data packet system for aerospace complex products based on business intelligence,
JVCIR(59), 2019, pp. 439-447.
Elsevier DOI 1903
Balanced scorecard, Business intelligence, Data warehouse, Quality data package, Polymorphic data, Complex product BibRef

Santra, B.[Bikash], Mukherjee, D.P.[Dipti Prasad],
A comprehensive survey on computer vision based approaches for automatic identification of products in retail store,
IVC(86), 2019, pp. 45-63.
Elsevier DOI 1906
Survey, Product detection, Product recognition, Planogram compliance, Multiple object detection, Out-of-stock detection BibRef

Adan, A.[Antonio], de la Rubia, D.[David],
Reconstruction of As-is Semantic 3D Models of Unorganised Storehouses,
3DV19(367-375)
IEEE DOI 1911
Image color analysis, Solid modeling, Semantics, IEEE merchandise, Classification algorithms, Monitoring, Object recognition BibRef

Kirkpatrick, K.[Keith],
Tracking Shoppers,
CACM(63), No. 1, January 2020, pp. 19-21.
DOI Link 2001
BibRef

Schrijvers, R.[Robin], Puttemans, S.[Steven], Callemein, T., Goedemé, T.[Toon],
Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis,
ACIVS20(541-553).
Springer DOI 2003
BibRef

Cao, Z.H.[Zhi-Hao], Mu, S.M.[Shao-Min], Dong, M.P.[Meng-Ping],
Two-attribute e-commerce image classification based on a convolutional neural network,
VC(36), No. 8, August 2020, pp. 1619-1634.
WWW Link. 2007
BibRef

Wang, K.[Kai], Zhang, T.T.[Tian-Tian], Xue, T.Q.[Tian-Qiao], Lu, Y.[Yu], Na, S.G.[Sang-Gyun],
E-commerce personalized recommendation analysis by deeply-learned clustering,
JVCIR(71), 2020, pp. 102735.
Elsevier DOI 2009
Clustering algorithm, Deep learning, Recommendation system BibRef

Santra, B.[Bikash], Shaw, A.K.[Avishek Kumar], Mukherjee, D.P.[Dipti Prasad],
Graph-based non-maximal suppression for detecting products on the rack,
PRL(140), 2020, pp. 73-80.
Elsevier DOI 2012
Detection, Grocery products, Non maximal suppression, Directed acyclic graph, R-CNN BibRef

Pei, T.[Tao], Liu, Y.X.[Ya-Xi], Shu, H.[Hua], Ou, Y.[Yang], Wang, M.[Meng], Xu, L.M.[Lian-Ming],
What Influences Customer Flows in Shopping Malls: Perspective from Indoor Positioning Data,
IJGI(9), No. 11, 2020, pp. xx-yy.
DOI Link 2012
BibRef

Spera, E., Furnari, A., Battiato, S., Farinella, G.M.,
EgoCart: A Benchmark Dataset for Large-Scale Indoor Image-Based Localization in Retail Stores,
CirSysVideo(31), No. 4, April 2021, pp. 1253-1267.
IEEE DOI 2104
Cameras, Pose estimation, Robot vision systems, Benchmark testing, Task analysis, shopping cart localization BibRef

Santra, B.[Bikash], Shaw, A.K.[Avishek Kumar], Mukherjee, D.P.[Dipti Prasad],
Part-based annotation-free fine-grained classification of images of retail products,
PR(121), 2022, pp. 108257.
Elsevier DOI 2109
Fine-grained classification, Reconstruction-classification network, Retail product detection BibRef

Wang, J.K.[Jia-Kai], Liu, A.[Aishan], Bai, X.[Xiao], Liu, X.L.[Xiang-Long],
Universal Adversarial Patch Attack for Automatic Checkout Using Perceptual and Attentional Bias,
IP(31), 2022, pp. 598-611.
IEEE DOI 2112
Deep learning, Visualization, Perturbation methods, Feature extraction, Training, Prototypes, Uncertainty, bias-based attack BibRef

Zhang, L.[Lu], Shen, J.[Jialie], Zhang, J.[Jian], Xu, J.S.[Jing-Song], Li, Z.B.[Zhi-Bin], Yao, Y.Z.[Ya-Zhou], Yu, L.T.[Li-Tao],
Multimodal Marketing Intent Analysis for Effective Targeted Advertising,
MultMed(24), No. 2022, pp. 1830-1843.
IEEE DOI 2204
Media, Advertising, Feature extraction, Social networking (online), Task analysis, Springs, Visualization, Multimodal, targeted advertising BibRef

Chen, H.[Hao], Zhou, Y.Z.[Yang-Zhun], Li, J.[Jun], Wei, X.S.[Xiu-Shen], Xiao, L.[Liang],
Self-Supervised Multi-Category Counting Networks for Automatic Check-Out,
IP(31), 2022, pp. 3004-3016.
IEEE DOI 2205
Task analysis, Training, Annotations, Testing, Object detection, Feature extraction, Deep learning, Automatic check-out, multi-category counting BibRef

Santra, B.[Bikash], Ghosh, U.[Udita], Mukherjee, D.P.[Dipti Prasad],
Graph-based modelling of superpixels for automatic identification of empty shelves in supermarkets,
PR(127), 2022, pp. 108627.
Elsevier DOI 2205
Gap detection, Retail store, Graph convolutional network, Siamese network, Structural support vector machine BibRef

Guo, Z.Y.[Zhao-Yu], Zhao, Z.[Zhou], Jin, W.[Weike], Wang, D.Z.[Da-Zhou], Liu, R.T.[Rui-Tao], Yu, J.[Jun],
TaoHighlight: Commodity-Aware Multi-Modal Video Highlight Detection in E-Commerce,
MultMed(24), 2022, pp. 2606-2616.
IEEE DOI 2205
Task analysis, Visualization, Feature extraction, Data models, Streaming media, Linguistics, Convolution, multi-modal learning BibRef

Siddiqui, T.[Tarique], Luh, P.[Paul], Wang, Z.[Zesheng], Karahalios, K.[Karrie], Parameswaran, A.G.[Aditya G.],
Expressive Querying for Accelerating Visual Analytics,
CACM(65), No. 7, July 2022, pp. 85-94.
DOI Link 2205
BibRef

Dong, X.[Xiao], Zhang, G.[Gengwei], Zhan, X.[Xunlin], Ding, Y.[Yi], Wei, Y.C.[Yun-Chao], Lu, M.[Minlong], Liang, X.D.[Xiao-Dan],
Caption-Aided Product Detection via Collaborative Pseudo-Label Harmonization,
MultMed(25), 2023, pp. 1916-1927.
IEEE DOI 2306
Training, Detectors, Electronic commerce, Noise measurement, Head, Proposals, Object detection, Product detection, pseudo-label, positive mining BibRef

Li, H.Y.[Hao-Yuan], Jiang, H.[Hao], Jin, T.[Tao], Li, M.Y.[Meng-Yan], Chen, Y.[Yan], Lin, Z.J.[Zhi-Jie], Zhao, Y.[Yang], Zhao, Z.[Zhou],
DATE: Domain Adaptive Product Seeker for E-Commerce,
CVPR23(19315-19324)
IEEE DOI 2309
BibRef

Chen, H.[Hao], Wei, X.S.[Xiu-Shen], Xiao, L.[Liang],
Prototype Learning for Automatic Check-Out,
MultMed(25), 2023, pp. 9147-9160.
IEEE DOI 2312
BibRef

Chen, H.[Hao], Wei, X.S.[Xiu-Shen], Zhang, F.[Faen], Shen, Y.[Yang], Xu, H.[Hui], Xiao, L.[Liang],
Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars,
ECCV22(XXV:277-293).
Springer DOI 2211
BibRef

Liu, Y.[Yuan],
Product Image Recommendation with Transformer Model Using Deep Reinforcement Learning,
IJIG(23), No. 6 2023, pp. 2550020.
DOI Link 2312
BibRef


Bai, X.H.[Xue-Han], Li, Y.[Yan], Cheng, Y.H.[Yan-Hua], Yang, W.J.[Wen-Jie], Chen, Q.[Quan], Li, H.[Han],
Cross-Domain Product Representation Learning for Rich-Content E-Commerce,
ICCV23(5674-5683)
IEEE DOI 2401
BibRef

Yang, W.J.[Wen-Jie], Chen, Y.[Yiyi], Li, Y.[Yan], Cheng, Y.H.[Yan-Hua], Liu, X.D.[Xu-Dong], Chen, Q.[Quan], Li, H.[Han],
Cross-view Semantic Alignment for Livestreaming Product Recognition,
ICCV23(13358-13367)
IEEE DOI Code:
WWW Link. 2401
BibRef

de Simone, G.[Giuseppe], Foggia, P.[Pasquale], Saggese, A.[Alessia], Vento, M.[Mario],
Autonomous mobile robot for automatic out of stock detection in a supermarket,
ACVR23(1821-1830)
IEEE DOI 2401
BibRef

Li, Z.X.[Zhi-Xuan], Ye, W.N.[Wei-Ning], Terven, J.[Juan], Bennett, Z.[Zachary], Zheng, Y.[Ying], Jiang, T.T.[Ting-Ting], Huang, T.J.[Tie-Jun],
MUVA: A New Large-Scale Benchmark for Multi-view Amodal Instance Segmentation in the Shopping Scenario,
ICCV23(23447-23456)
IEEE DOI 2401
BibRef

Strohmayer, J.[Julian], Kampel, M.[Martin],
Domain-adaptive Data Synthesis for Large-scale Supermarket Product Recognition,
CAIP23(I:239-250).
Springer DOI 2312
BibRef

Morán, E.F.[Emmanuel F.], Vintimilla, B.X.[Boris X.], Realpe, M.A.[Miguel A.],
Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves,
CIARP23(I:257-271).
Springer DOI 2312
BibRef

Strohmayer, J.[Julian], Kampel, M.[Martin],
Real-Time Supermarket Product Recognition on Mobile Devices Using Scalable Pipelines,
ICIP23(420-424)
IEEE DOI 2312
BibRef

Ma, Z.L.[Ze-Liang], Liu, D.[Delong], Cui, Z.[Zhe], Zhao, Y.[Yanyun],
AdaptCD: An Adaptive Target Region-based Commodity Detection System,
AICity23(5486-5495)
IEEE DOI 2309
BibRef

Cai, Y.C.[Yi-Chen], Jiao, A.[Aoran],
DACNet: A Deep Automated Checkout Network with Selective Deblurring,
AICity23(5278-5286)
IEEE DOI 2309
BibRef

Jin, Y.[Yang], Li, Y.Z.[Yong-Zhi], Yuan, Z.H.[Ze-Huan], Mu, Y.D.[Ya-Dong],
Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce,
CVPR23(11060-11069)
IEEE DOI 2309
BibRef

Shi, Z.Q.[Zi-Qiang], Liu, Z.L.[Zhong-Ling], Liu, L.[Liu], Liu, R.J.[Ru-Jie], Yamamoto, T.[Takuma], Mi, X.Y.[Xiao-Yu], Uchida, D.[Daisuke],
CheckSORT: Refined Synthetic Data Combination and Optimized SORT for Automatic Retail Checkout,
AICity23(5391-5398)
IEEE DOI 2309
BibRef

Dhonde, A.[Anudeep], Guntur, P.[Prabhudev], Palani, V.[Vinitha],
Adaptive RoI with pretrained models for Automated Retail Checkout,
AICity23(5507-5510)
IEEE DOI 2309
BibRef

Vats, A.[Arpita], Anastasiu, D.C.[David C.],
Enhancing Retail Checkout through Video Inpainting, YOLOv8 Detection, and DeepSort Tracking,
AICity23(5530-5537)
IEEE DOI 2309
BibRef

Ghosh, P.[Pushpendu], Wang, N.[Nancy], Yenigalla, P.[Promod],
D-Extract: Extracting Dimensional Attributes From Product Images,
WACV23(3630-3638)
IEEE DOI 2302
Computational modeling, Computer network reliability, Transformers, Information filters, Data models, Vision + language and/or other modalities BibRef

Wang, J.[Jing], Liu, J.[Jun], Xia, Z.W.[Zhi-Wei], Chen, P.[Peng], Li, X.[Xin], Chen, X.[Xiao],
Semi-supervised Labeling Model Based on Gaussian Mixture in the Context of E-commerce Price Fraud,
ICRVC22(300-304)
IEEE DOI 2301
Interpolation, Annotations, Computational modeling, Data models, Regulation, Fraud, Labeling, price fraud, multiple imputations, CDBN network BibRef

Chen, F.Y.[Fang-Yi], Zhang, H.[Han], Li, Z.W.[Zai-Wang], Dou, J.C.[Jia-Chen], Mo, S.T.[Shen-Tong], Chen, H.[Hao], Zhang, Y.X.[Yong-Xin], Ahmed, U.[Uzair], Zhu, C.C.[Chen-Chen], Savvides, M.[Marios],
Unitail: Detecting, Reading, and Matching in Retail Scene,
ECCV22(VII:705-722).
Springer DOI 2211
BibRef

Wu, J.[Junde], Zhang, Y.[Yu], Fu, R.[Rao], Liu, Y.P.[Yuan-Pei], Gao, J.[Jing],
An Efficient Person Clustering Algorithm for Open Checkout-free Groceries,
ECCV22(XXXVIII:17-33).
Springer DOI 2211
BibRef

Bartl, V.[Vojtech], Španhel, J.[Jakub], Herout, A.[Adam],
PersonGONE: Image Inpainting for Automated Checkout Solution,
AICity22(3114-3122)
IEEE DOI 2210
Deep learning, Image segmentation, Image recognition, Urban areas, Neural networks, Detectors BibRef

Pham, L.H.[Long Hoang], Tran, D.N.N.[Duong Nguyen-Ngoc], Nguyen, H.H.[Huy-Hung], Jeon, H.J.[Hyung-Joon], Tran, T.H.P.[Tai Huu-Phuong], Jeon, H.M.[Hyung-Min], Jeon, J.W.[Jae Wook],
Improving Deep Learning-based Automatic Checkout System Using Image Enhancement Techniques,
AICity23(5333-5340)
IEEE DOI 2309
BibRef
Earlier: A1, A2, A3, A5, A4, A6, A7:
DeepACO: A Robust Deep Learning-based Automatic Checkout System,
AICity22(3106-3113)
IEEE DOI 2210
Training, Image resolution, Tracking, Pipelines, Urban areas, Benchmark testing BibRef

Shihab, M.I.H.[Md. Istiak Hossain], Tasnim, N.[Nazia], Zunair, H.[Hasib], Rupty, L.K.[Labiba Kanij], Mohammed, N.[Nabeel],
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout,
AICity22(3182-3190)
IEEE DOI 2210
Measurement, Training, Image segmentation, Urban areas, Video sequences, Transformers, Entropy BibRef

Shoman, M.[Maged], Aboah, A.[Armstrong], Morehead, A.[Alex], Duan, Y.[Ye], Daud, A.[Abdulateef], Adu-Gyamfi, Y.[Yaw],
A Region-Based Deep Learning Approach to Automated Retail Checkout,
AICity22(3209-3214)
IEEE DOI 2210
Deep learning, Training, Runtime, Shape, Urban areas, Pipelines, Reliability BibRef

Wan, J.F.[Jun-Feng], Qian, S.H.[Shu-Hao], Tian, Z.[Zihan], Zhao, Y.Y.[Yan-Yun],
An Effective Framework of Multi-Class Product Counting and Recognition for Automated Retail Checkout,
AICity22(3281-3289)
IEEE DOI 2210
Training, Codes, Training data, Trajectory, Pattern recognition BibRef

Pietrini, R.[Rocco], Rossi, L.[Luca], Mancini, A.[Adriano], Zingaretti, P.[Primo], Frontoni, E.[Emanuele], Paolanti, M.[Marina],
A Deep Learning-Based System for Product Recognition in Intelligent Retail Environment,
CIAP22(II:371-382).
Springer DOI 2205
BibRef

Greco, A.[Antonio], Saggese, A.[Alessia], Vento, B.[Bruno],
A Robust and Efficient Overhead People Counting System for Retail Applications,
CIAP22(II:139-150).
Springer DOI 2205
BibRef

Mata, C.[Cristina], Locascio, N.[Nick], Sheikh, M.A.[Mohammed Azeem], Kihara, K.[Kenny], Fischetti, D.[Dan],
StandardSim: A Synthetic Dataset for Retail Environments,
CIAP22(II:65-76).
Springer DOI 2205
BibRef

He, Z.L.[Zhao-Liang], Wang, Y.[Yuan], Tang, C.[Chen], Wang, Z.[Zhi], Zhu, W.W.[Wen-Wu], Guo, C.Y.[Chen-Yang], Chen, Z.B.[Zhi-Bo],
AdaConfigure: Reinforcement Learning-Based Adaptive Configuration for Video Analytics Services,
MMMod22(I:245-257).
Springer DOI 2203
BibRef

Das, N.[Nilotpal], Joshi, A.[Aniket], Yenigalla, P.[Promod], Agrwal, G.[Gourav],
MAPS: Multimodal Attention for Product Similarity,
WACV22(2988-2996)
IEEE DOI 2202
Training, Representation learning, Measurement, Scalability, Training data, Benchmark testing, Vision and Languages BibRef

Jain, S.[Shubham], Schweiss, T.[Thomas], Bender, S.[Simon], Werth, D.[Dirk],
Omnichannel Retail Customer Experience with Mixed-Reality Shopping Assistant Systems,
ISVC21(I:504-517).
Springer DOI 2112
BibRef

Allegra, D.[Dario], Litrico, M.[Mattia], Spatafora, M.A.N.[Maria Ausilia Napoli], Stanco, F.[Filippo], Farinella, G.M.[Giovanni Maria],
Exploiting Egocentric Vision on Shopping Cart for Out-Of-Stock Detection in Retail Environments,
ACVR21(1735-1740)
IEEE DOI 2112
Deep learning, Annotations, Pipelines, Benchmark testing BibRef

Tomas, H.[Henri], Reyes, M.[Marcus], Dionido, R.[Raimarc], Ty, M.[Mark], Mirando, J.[Jonric], Casimiro, J.[Joel], Atienza, R.[Rowel], Guinto, R.[Richard],
GOO: A Dataset for Gaze Object Prediction in Retail Environments,
Gaze21(3119-3127)
IEEE DOI 2109
Training, Adaptation models, Estimation, Benchmark testing BibRef

Ciocca, G.[Gianluigi], Napoletano, P.[Paolo], Locatelli, S.G.[Simone Giuseppe],
Multi-task Learning for Supervised and Unsupervised Classification of Grocery Images,
VTIUR20(325-338).
Springer DOI 2103
BibRef

Ciocca, G.[Gianluigi], Napoletano, P.[Paolo], Locatelli, S.G.[Simone Giuseppe],
Iconic-based Retrieval of Grocery Images via Siamese Neural Network,
VTIUR20(269-281).
Springer DOI 2103
BibRef

Wen, J.H.[Jia-Hao], Guillen, L.[Luis], Amrizal, M.A.[Muhammad Alfian], Abe, T.[Toru], Suganuma, T.[Takuo],
An Event-based Hierarchical Method for Customer Activity Recognition in Retail Stores,
ISVC20(I:263-275).
Springer DOI 2103
BibRef

Sciucca, L.D.[Laura Della], Manco, D.[Davide], Contigiani, M.[Marco], Pietrini, R.[Rocco], di Bello, L.[Luigi], Placidi, V.[Valerio],
Shoppers Detection Analysis in an Intelligent Retail Environment,
DEEPRETAIL20(534-546).
Springer DOI 2103
BibRef

Marinelli, L.[Luca], Paolanti, M.[Marina], Nardi, L.[Lorenzo], Gabellini, P.[Patrizia], Frontoni, E.[Emanuele], Gregori, G.L.[Gian Luca],
Data-driven Knowledge Discovery in Retail: Evidences from the Vending Machine's Industry,
DEEPRETAIL20(508-520).
Springer DOI 2103
BibRef

Milella, A.[Annalisa], Marani, R.[Roberto], Petitti, A.[Antonio], Cicirelli, G.[Grazia], d'Orazio, T.[Tiziana],
3d Vision-based Shelf Monitoring System for Intelligent Retail,
DEEPRETAIL20(447-459).
Springer DOI 2103
BibRef

Bruno, A.[Alessandro], Lancette, S.[Stéphane], Zhang, J.L.[Jing-Lu], Moore, M.[Morgan], Ward, V.P.[Ville P.], Chang, J.[Jian],
A Saliency-based Technique for Advertisement Layout Optimisation to Predict Customers' Behaviour,
DEEPRETAIL20(495-507).
Springer DOI 2103
BibRef

Hong, Y., Shi-Qiang, G.,
Application Research of Interactive Packaging Design Based on Computer Graphics Technology and GIS Model,
CVIDL20(550-553)
IEEE DOI 2102
data mining, data visualisation, design engineering, geographic information systems, image representation, GIS model BibRef

Han, W., Huang, Z., kuerban, A., Yan, M., Fu, H.,
A Mask Detection Method for Shoppers Under the Threat of COVID-19 Coronavirus,
CVIDL20(442-447)
IEEE DOI 2102
convolutional neural nets, feature extraction, image classification, image representation, spatial separable convolution BibRef

Liu, A.[Aishan], Wang, J.K.[Jia-Kai], Liu, X.L.[Xiang-Long], Cao, B.[Bowen], Zhang, C.Z.[Chong-Zhi], Yu, H.[Hang],
Bias-based Universal Adversarial Patch Attack for Automatic Check-out,
ECCV20(XI:395-410).
Springer DOI 2011
BibRef

Nguyen, M.[Minh], Le, H.[Huy], Yan, W.Q.[Wei Qi],
Red-green-blue Augmented Reality Tags for Retail Stores,
ACIVS20(467-479).
Springer DOI 2003
BibRef

Shahriari Mehr, G., Delavar, M.R., Claramunt, C., Araabi, B.N., Dehaqani, M.R.A.,
Discover Points of Interest Based On Users' Internet Searches Through An Online Shopping Website,
SMPR19(975-980).
DOI Link 1912
BibRef

Zhao, L., Yao, J., Du, H., Zhao, J., Zhang, R.,
A Unified Object Detection Framework for Intelligent Retail Container Commodities,
ICIP19(3891-3895)
IEEE DOI 1910
Intelligent retail, object detection, non-maximum suppression BibRef

de Souza Junior, N.F.[Nelson Forte], da Silva, L.A.[Leandro Augusto], Marengoni, M.[Mauricio],
Product Recommendation Through Real-Time Object Recognition on Image Classifiers,
ICIAR19(II:40-51).
Springer DOI 1909
BibRef

Allegrino, F.[Fioravante], Gabellini, P.[Patrizia], di Bello, L.[Luigi], Contigiani, M.[Marco], Placidi, V.[Valerio],
The Vending Shopper Science Lab: Deep Learning for Consumer Research,
NTIAP19(307-317).
Springer DOI 1909
BibRef

Paolanti, M.[Marina], Pierdicca, R.[Roberto], Martini, M.[Massimo], di Stefano, F.[Francesco], Morbidoni, C.[Christian], Mancini, A.[Adriano], Malinverni, E.S.[Eva Savina], Frontoni, E.[Emanuele], Zingaretti, P.[Primo],
Semantic 3D Object Maps for Everyday Robotic Retail Inspection,
NTIAP19(263-274).
Springer DOI 1909
BibRef

Porta, S.L.[Salvatore La], Marconi, F.[Fabrizio], Lazzini, I.[Isabella],
Collecting Retail Data Using a Deep Learning Identification Experience,
NTIAP19(275-284).
Springer DOI 1909
BibRef

Gabellini, P.[Patrizia], d'Aloisio, M.[Mauro], Fabiani, M.[Matteo], Placidi, V.[Valerio],
A Large Scale Trajectory Dataset for Shopper Behaviour Understanding,
NTIAP19(285-295).
Springer DOI 1909
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Klasson, M., Zhang, C., Kjellström, H.,
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convolutional neural nets, electronic commerce, handicapped aids, image classification, learning (artificial intelligence) BibRef

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WVC17(114-119)
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AVSS13(135-140)
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Chapter on Motion -- Human Motion, Surveillance, Tracking, Surveillance, Activities continues in
Surveillance Systems, Applied to Fire and Flame Detection .


Last update:Mar 16, 2024 at 20:36:19