Name
Abstract | ||
|---|---|---|
This study proposes a method to identify a customer's interest in the product. Specifically, we applied the state-of-the-art deep learning algorithms to the real-world surveillance videos for analyzing customer interest in the product and evaluated the accuracy. For this, we first introduce a new first of its kind dataset called ICI (items of customer's interest) that includes various shopping situations. We experimented the state-of-the-art deep learning algorithms on the ICI dataset to determine a suitable algorithm for identifying a customer's interest. The experimental results demonstrated that the estimation accuracy is 71% on the average, meaning that a customer's interest can be measured effectively. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BigComp48618.2020.0-105 | 2020 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | DocType | ISSN |
Customer interest, Application of AI, Surveillance camera | Conference | 2375-933X |
ISBN | Citations | PageRank |
978-1-7281-6035-1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Jun Lee | 1 | 0 | 1.35 |
| U.-ju Gim | 2 | 0 | 0.34 |
| Jeong-Hun Kim | 3 | 3 | 1.79 |
| Kwan-Hee Yoo | 4 | 0 | 0.34 |
| Young-Ho Park | 5 | 137 | 16.79 |
| Aziz Nasridinov | 6 | 49 | 14.32 |