[论文解读] Novel and Automatic Parking Inventory System Based on Pattern Recognition and Directional Chain Code
本文提出了一种基于模式识别和方向链码的自动停车库库存系统,用于实时检测和识别车辆牌照。该系统在波斯语车牌上的牌照定位准确率达到100%,字符识别准确率达到99%,实现了车辆自动追踪、费用计算和道闸控制。
The objective of this paper is to design an efficient vehicle license plate recognition System and to implement it for automatic parking inventory system. The system detects the vehicle first and then captures the image of the front view of the vehicle. Vehicle license plate is localized and characters are segmented. For finding the place of plate, a novel and real time method is expressed. A new and robust technique based on directional chain code is used for character recognition. The resulting vehicle number is then compared with the available database of all the vehicles so as to come up with information about the vehicle type and to charge entrance cost accordingly. The system is then allowed to open parking barrier for the vehicle and generate entrance cost receipt. The vehicle information (such as entrance time, date, and cost amount) is also stored in the database to maintain the record. The hardware and software integrated system is implemented and a working prototype model is developed. Under the available database, the average accuracy of locating vehicle license plate obtained 100%. Using 70% samples of character for training, we tested our scheme on whole samples and obtained 100% correct recognition rate. Further we tested our character recognition stage on Persian vehicle data set and we achieved 99% correct recognition.
研究动机与目标
- 开发一种高效、自动化的停车库库存系统,利用牌照识别技术。
- 解决在停车环境中实时、准确检测和识别车辆牌照的挑战。
- 将车辆识别与自动道闸控制及费用计算相结合。
- 确保在牌照定位和字符识别方面达到高准确率,特别是针对波斯语等非拉丁字母脚本。
- 实现一个可工作的原型系统,用于存储车辆数据(入场时间、费用),并支持数据库驱动的操作。
提出的方法
- 使用一种实时方法检测车辆并捕获正面图像。
- 应用一种新技术在捕获的车辆图像中定位牌照。
- 采用方向链码作为鲁棒的特征提取方法,用于字符识别。
- 从定位到的牌照区域中分割出单个字符。
- 将识别出的车牌号码与预先存在的数据库进行比对,以确定车辆类型和入场费用。
- 集成软硬件组件,以控制停车道闸并生成数字收据。
实验结果
研究问题
- RQ1如何在停车库库存系统中实现实时自动化的牌照检测与识别?
- RQ2方向链码在识别车辆牌照上的字符,特别是非拉丁字母脚本中的字符时,效果如何?
- RQ3该系统在真实世界条件下能否实现牌照定位和字符识别的高准确率?
- RQ4该系统在波斯语车牌上的表现如何,这些车牌可能具有复杂的字符形状和脚本?
- RQ5该系统能否可靠地集成到完整的停车管理流程中,包括道闸控制和数据记录?
主要发现
- 在测试数据库条件下,该系统在牌照定位方面达到了100%的准确率。
- 使用70%的字符样本进行训练时,识别系统在完整测试集上实现了100%的正确识别率。
- 在专用的波斯语车牌数据集上,字符识别准确率达到99%。
- 该系统成功实现了车辆识别与自动道闸控制及收据生成的集成。
- 已开发并验证了一个功能原型,展示了实时性能和数据记录能力。
- 方向链码方法在复杂或非标准字符集下也表现出鲁棒性和有效性,适用于字符识别。
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