A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Toward Category-Level Object Recognition (Lecture Notes in Computer Science)
Template Matching Techniques in Computer Vision: Theory and Practice
Template Matching Techniques in Computer Vision: Theory and Practice
| Hi-index | 0.00 |
This paper describes a computer vision system to detect card counters and dealer errors in a game of Blackjack from an overhead stereo camera. Card counting is becoming increasingly popular among casual Blackjack players, and casinos are eager to find new systems of dealing with the issue. There are several existing systems on the market; however, these solutions tend to be overly expensive, require specialised hardware (e.g. RFID) and are only cost-effective to the largest casinos. With a user-centered design approach, we built a simple and effective system that detects cards and player bets in real time, and calculates the correlation between player bets and the card count to determine if a player is card counting. The system uses a combination of contour analysis, template matching and the SIFT algorithm to detect and recognise cards. Stereo imaging is used to calculate the height of chip stacks on the table, allowing the system to track the size of player bets. Our system achieves card recognition accuracy of over 99%, and effectively detected card counters and dealer errors when tested with a range of different users, including professional dealers and novice blackjack players.