Name
Abstract | ||
|---|---|---|
A significant portion of retail shrink is attributed to employees and occurs around the point of sale (POS). In this paper, we target a major type of retail fraud in surveillance videos, known as sweethearting (or fake scan), where a cashier intentionally fails to enter one or more items into the transaction in an attempt to get free merchandise for the customer. We first develop a motion-based algorithm to identify video segments as candidates for primitive events at the POS. We then apply spatio-temporal features to recognize true primitive events from the candidates and prune those falsely alarmed. In particular, we learn location-aware event models by Multiple-Instance Learning to address the location-sensitive issues that appear in our problem. Finally, we validate the entire transaction by combining primitive events according to temporal ordering constraints. We demonstrate the effectiveness of our approach on data captured from a real grocery store. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICASSP.2009.4959867 | ICASSP |
Keywords | Field | DocType |
index terms— retail shrink,free merchandise,motion-based algorithm,true primitive event,retail surveillance video,location-aware event model,event recognition,major type,detecting sweethearting,primitive event,entire transaction,retail fraud,multiple-instance learning,location-sensitive issue,bagging,indexing terms,optimization problem,image segmentation,background subtraction,face detection,visualization,viterbi algorithm,transaction processing,computer vision,data mining,point of sale,false positive,data capture,merchandise,pattern recognition,hidden markov models | Data mining,Computer science,Image segmentation,Artificial intelligence,Face detection,Database transaction,Event recognition,Pattern recognition,Information retrieval,Visualization,Point of sale,Hidden Markov model,Product (business) | Conference |
ISSN | Citations | PageRank |
1520-6149 | 7 | 0.63 |
References | Authors | |
9 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Quanfu Fan | 1 | 504 | 32.69 |
| Akira Yanagawa | 2 | 278 | 23.69 |
| Russell Bobbitt | 3 | 30 | 3.04 |
| Yun Zhai | 4 | 735 | 32.59 |
| Rick Kjeldsen | 5 | 433 | 108.43 |
| Sharath Pankanti | 6 | 3542 | 292.65 |
| Arun Hampapur | 7 | 1106 | 209.27 |