中文摘要：

提出了一种基于流式大规模车牌识别数据集的伴随车辆（伴随车辆是指在一段持续的时间内一起移动的车辆组群）即时发现方法,可实现即时发现疑似伴随车辆并将其按伴随概率排序.该方法充分利用了云基础设施的并行计算能力,基于整数划分思想建立并行发现的负载均衡模型,优化了伴随车辆的发现性能,可用于对时间敏感的交通应用场景,如发现并监控运钞车等特殊车辆的跟踪车辆等.实验证明,该方法能够有效处理大规模的流式车牌识别数据,并实时地输出发现结果.

英文摘要：

Traveling companions are object groups that move together in a period of time. To quickly identify traveling companions from a special kind of streaming traffic data, called automatic number plate recognition （ANPR） data, a framework and several algorithms were presented to discover companion vehicles, which can instantly detect suspicious companion vehicles with their probabilities when they pass through monitoring cameras. The framework can he used in many time-sensitive scenarios like taking surveillance on suspect trackers for specific vehicles. Experiments show that the proposed approach can process streaming ANPR data directly and discover companion vehicles in nearly real time.