Logging of incoming/outgoing vehicles serves as a piece of critical information for root-cause analysis to combat security breach incidents in various sensitive organizations. RFID tagging hampers the scalability of vehicle tracking solutions on both logistics as well as technical fronts. For instance, requiring each incoming vehicle(departmental or private) to be RFID tagged is a severe constraint and coupling video analytics with RFID to detect abnormal vehicle movement is non-trivial. We leverage publicly available implementations of computer vision algorithms to develop an interpretable vehicle tracking algorithm using finite-state machine formalism. The state-machine consumes input from the cascaded object detection and optical character recognition(OCR) models for state transitions. We evaluated the proposed method on 75 video clips of 285 vehicles from our system deployment site. We observed that the detection rate is most affected by the speed and the type of vehicle. The highest detection rate is achieved when the vehicle movement is restricted to follow a movement restrictions(SOP) at the checkpoint similar to RFID tagging. We further analyzed 700 vehicle tracking predictions on live-data and identified that the majority of vehicle number prediction errors are due to illegible-text, image-blur, text occlusion and out-of-vocab letters in vehicle numbers. Towards system deployment and performance enhancement, we expect our ongoing system monitoring to provide evidences to establish a higher vehicle-throughput SOP at the security checkpoint as well as to drive the fine-tuning of the deployed computer-vision models and the state-machine to establish the proposed approach as a promising alternative to RFID-tagging.
翻译:为打击各敏感组织的安全违规事件,对进出车辆进行登录是一个关键分析关键信息,用于各敏感组织进行根本原因分析,以打击违反安全事件。RFID标记妨碍了车辆跟踪解决方案在后勤和技术战线上的可扩展性。例如,要求每部或私人车辆在每部或私人车辆上贴RFID标签是一项严重限制,并将视频分析器与RFID合为一体,以探测车辆的异常流动情况,这是非三角的。我们利用公开提供的计算机视觉算法,利用固定状态机器正规化方法来开发可解释的车辆跟踪算法。州机器利用级物体探测和光学识别模型为州过渡提供的投入。我们评估了系统部署地点285部车辆75个视频夹的拟议方法。我们发现,探测率受车辆速度和类型影响最大。当车辆移动受到限制,在与RFID标记方法相似的检查站遵循行动限制(SOPSOP),我们进一步分析了700部对现场数据进行跟踪的预测,并查明大多数车辆数量预测误差是我们目前部署的轨道预测,这是我们不断改进的车辆部署的系统,这是不断改进的系统,以便显示,我们不断改进的车辆的文本,显示,我们将改进的系统将改进的系统将改进的文本,将改进的文本,以显示,以显示,将改进的改进的系统将改进的文本,以显示,以显示我们将改进的文本,将改进的文本,将改进的文本,以显示,将改进的文本,将改进车辆的改进的文本,将改进的文本,将改进车辆的文本,以显示我们为可改进的成绩。