Automated 3D Vision Tracking for Project Control Support

On-site tracking in open construction sites is often difficult because of the large amounts of items that are present and need to be tracked. Additionally, the amounts of occlusions/obstructions present create a highly complex tracking environment. Existing tracking methods are based mainly on Radio Frequency technologies, including Global Positioning Systems (GPS), Radio Frequency Identification (RFID), Bluetooth and Wireless Fidelity (Wi-Fi, Ultra-Wideband, etc). These methods require considerable amounts of pre-processing time since they need to manually deploy tags and keep record of the items they are placed on. In construction sites with numerous entities, tags installation, maintenance and decommissioning become an issue since it increases the cost and time needed to implement these tracking methods. This paper presents a novel method for open site tracking with construction cameras based on machine vision. According to this method, video feed is collected from on site video cameras, and the user selects the entity he wishes to track. The entity is tracked in each video using 2D vision tracking. Epipolar geometry is then used to calculate the depth of the marked area to provide the 3D location of the entity. This method addresses the limitations of radio frequency methods by being unobtrusive and using inexpensive, and easy to deploy equipment. The method has been implemented in a C++ prototype and preliminary results indicate its effectiveness

Correlating Multiple 2D Vision Trackers for 3D Object Tracking on Construction Sites

Tracking methods have the potential to retrieve the spatial location of project related entities such as personnel and equipment at construction sites, which can facilitate several construction management tasks. Existing tracking methods are mainly based on Radio Frequency (RF) technologies and thus require manual deployment of tags. On construction sites with numerous entities, tags installation, maintenance and decommissioning become an issue since it increases the cost and time needed to implement these tracking methods. To address these limitations, this paper proposes an alternate 3D tracking method based on vision. It operates by tracking the designated object in 2D video frames and correlating the tracking results from multiple pre-calibrated views using epipolar geometry. The methodology presented in this paper has been implemented and tested on videos taken in controlled experimental conditions. Results are compared with the actual 3D positions to validate its performance.