Abstract

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Title:: Multiple articulated construction machinery efficiency analysis method based on local feature reidentification

Author(s):: LU Yujie; ZHONG Lijian; WEI Wei; WANG Shuo; 1. College of Civil Engineering, Tongji University, Shanghai 200092, China； 2. Shanghai Research Institute for Intelligent Autonomous System, Tongji University, Shanghai 201210, China

Keywords:: multiobject re-identification; local feature matching; multi-object tracking; machinery efficiency analysis; construction machinery management

Abstract:: In order to overcome the interruption issues caused by occlusion in existing vision-based machinery efficiency analysis methods applied to articulated construction machinery, and to achieve longterm and intelligent efficient construction machinery efficiency analysis, a method for efficiency analysis of multiple articulated construction machinery based on local feature reidentification was proposed. The proposed method utilized convolutional neural networks to extract local appearance features of the excavator for identity matching and target re-identification, effectively correcting the misjudgment of multiple target identities caused by occlusion. Based on multiobject tracking, the activity recognition algorithms to achieve statistical analysis of multiple excavators activities and efficiency analysis were further applied. Taking a construction project in Shanghai as a case study, the excavator on the construction site was tracked and the efficiency was analyzed. The results show that the results of reidentification and stable tracking (with a video duration of 25 minutes) using this method are more accurate, with the accuracy of machinery efficiency analysis reaching 96.74%. This study provides a research paradigm for multi-mechanical target re-identification in the construction engineering field and offers a practical example for intelligent efficiency analysis based on visual methods in complex scenarios.