电梯检验视频的缓冲器行为AI跟踪判定方法研究
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中图分类号：TP391.4；TU857 文献标识码：A 文章编号：2096-4706（2025）22-0023-07
Research on Al Tracking and Judgment Method for Buffer Behavior in Elevator Inspection Videos
LUO Weili’， CHEN Guilong，LIU Guixiong，CHEN Jianxun （1.Zhuhai Anyue Technology Co.，Ltd.， Zhuhai 519ooo， China; 2.SchoolofMechanical andAutomotiveEngineering，South China UniversityofTechnology，Guangzhou ，China; 3.Guangdong Institute of Special Equipment Inspection and Research Zhuhai Branch， Zhuhai 519ooo， China）
Abstract：Thenumberof elevators inservice is huge，and their periodic inspections formamasive video database.Manual reviewofvideocompliancehasproblemsoflowefciencyandlowreliability.Toaddressthis，thispaperstudiesabuferbehavior trackingand judgment method basedonMask R-CNN + SORT+HeightSeqAnalyzer. It uses Mask R-CNN and SORT algorithms respectivelytoachievehigh-precisiondetectionofbuferobjects insingle framesandobjecttracking inthetemporaldimnsion. ItcalculatestereferenceheightofbjectstroughthepixelheightsequenceoftheHeightSeqAnalyzeralgorithm，judgesobject compressionbehavioraccordingtothecompressiondegree thresholdandcompression duration threshold，and finallymerges compresion eventsbasedontheoverlapofcompression frame sets tooutput the numberofbufercompresson events inthe video. Atraining setandavalidationsetareconstructedthroughorthogonal experimental design totrain the Object Detection model. Experiments show that in image-level detection，the method in this paper achieves a precision P=0.81 ，recall R=0.78 ，mAP@0.75-0.72， and mAP@0.5：0.95=0.63 when both the IoU threshold and confidence threshold are 0.75.In video-level judgment， the precision P （2号 and specificity Sp both reach 1.00，and the accuracy A=0.84 ，completely eliminating FP samples，which verifies the effectiveness and engineering applicabilityof the method in this paper for automatic review ofelevator inspectionvideo compliance.
Keywords： behavior determination; Object Detection; object tracking; elevator inspection
0 引言
电梯作为城市基础设施重要组成部分，关系到公共民生保障，其运行安全受到高度关注[。（剩余12879字）
