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Hybrid object detection and marker recognition system to monitor performance of the hauling dump trucks

International Construction Specialty Conference (5th : 2015 : Vancouver, B.C.); Canadian Society for Civil Engineering

Various sensing technologies have been developed for real-time monitoring of the earthmoving fleet on construction and surface mining jobsites. Computer vision-based methods are among the most recent techniques employed to track earthmoving machines in a construction field. All the research efforts in this area investigated computer vision algorithms to detect and track different types of equipment, but they were unable to identify individual machines within the fleet. This paper introduces a hybrid system which uses a combination of an object detection method and a marker recognition algorithm to identify individual dump trucks using specific markers attached on them. Background subtraction and Histogram of Oriented Gradients (HOG) algorithm were used to detect candidates in the video frames and then the system zooms on the detected bounding boxes to obtain a better resolution for marker detection. Next, the marker recognition module searches the zoomed frame for a marker and in case of successful identification; it verifies the detection and records a trip for that individual truck. The results showed promising performance, in which the system identified 83% of the hauling trips made by the marked machines without producing any false positives.

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2015-05-26

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/53508

Froese, T. M., Newton, L., Sadeghpour, F. & Vanier, D. J. (EDs.) (2015). Proceedings of ICSC15: The Canadian Society for Civil Engineering 5th International/11th Construction Specialty Conference, University of British Columbia, Vancouver, Canada. June 7-10.

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