Large-scale earthmoving activities make up a costly and air-polluting aspect of many construction projects and mining operations, which depend entirely on the use of heavy construction equipment. The long-term jobsites and manufacturing nature of the mining sector has encouraged the application of automated controlling systems, more specifically GPS, to control the earthmoving fleet. Computer vision-based methods are another potential tool to provide real-time information at low-cost and to reduce human error in surface earthmoving sites as relatively clear views can be selected and the equipment offer recognizable targets. Vision-based methods have some advantages over positioning devices as they are not intrusive, provide detailed data about the behaviour of each piece of equipment, and offer reliable documentation for future reviews. This dissertation explains the development of a vision-based system, named server-customer interaction planner (SCIT), to recognize and estimate earth material loading cycles. The SCIT system consists of three main modules: object recognition, tracking, and action recognition. Different object recognition and tracking algorithms were evaluated and modified, and then the ideal methods were used to develop the object recognition and tracking modules. A novel hybrid tracking framework was developed for the SCIT system to track dump trucks in the challenging views found in the loading zones. The object recognition and tracking engines provide spatiotemporal data about the equipment which are then analyzed by the action recognition module to estimate loading cycles. The entire framework was evaluated using videos taken under varying conditions. The results highlight the promising performance of the SCIT system with the hybrid tracking engine, thereby validating the possibility of its practical application.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/35938 |
| Date | 09 August 2013 |
| Creators | Rezazadeh Azar, Ehsan |
| Contributors | McCabe, Brenda |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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