基于卡口监测数据流的套牌车检测

摘要 套牌车行为不仅扰乱公共交通秩序，还危害了真实车主的利益，给社会带来了极大危害，整治套牌车刻不容缓.广泛使用的卡口时间对比法使用统一的速度阈值检测套牌车辆，一旦阈值设置不当，容易造成套牌误判和漏检，导致检测结果准确率低.针对这样的问题，本文提出了一种两阶段的套牌车检测框架.离线部分根据历史卡口摄像头监测数据分时段对各路段建立速度分布，确定不同时段各路段的正常速度阈值；在线部分基于滑动窗口模型，根据正常速度阈值，对各路段实时通行车辆进行持续监测，将高频度出现速度异常的车辆判定为套牌车.最后使用真实卡口监测数据集对所提方法进行有效性验证，实验结果表明该方法能够有效避免噪声数据干扰，从而显著提升了套牌车检测的准确率.
Abstract：Since vehicles using fake plate violate traffic laws and regulations, infringe the rights of legal license owners and harm social benefits, it is therefore of great urgency to solve this social problem. In current detection methods, unified speed threshold is used to detect fake plate vehicles. Once an inappropriate threshold was set, it would lead to misjudgements and omissions, and thereby results in a low judgement accuracy. To solve this problem, we propose a two-phase fake plate vehicles detection framework. In the offline part, we train a distributed speed model based on sparse traffic trajectory data to calculate time-dependent thresholds for different roads. In the online part, we apply a sliding-window method to monitor whether a car is an outlier. If a car is continually detected as an outlier, it will be judged as a fake, and vice versa. We then use a real dataset to evaluate our method. The results demonstrate negative influences of noise data can be avoided by our method, so the accuracy of fake plate vehicles detection can be improved significantly.