VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Heydarian, Arsalan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Automated Vision-Based Tracking and Action Recognition of Earthmoving Construction Operations
    Heydarian, Arsalan (Virginia Tech, 2012-04-30)
    The current practice of construction productivity and emission monitoring is performed by either manual stopwatch studies which are significantly labor intensive and subject to human errors, or by the use of RFID and GPS tracking devices which may be costly and impractical. To address these limitations, a novel computer vision based method for automated 2D tracking, 3D localization, and action recognition of construction equipment from different camera viewpoints is presented. In the proposed method, a new algorithm based on Histograms of Oriented Gradients and hue-saturation Colors (HOG+C) is used for 2D tracking of the earthmoving equipment. Once the equipment is detected, using a Direct Linear Transformation followed by a non-linear optimization, their positions are localized in 3D. In order to automatically analyze the performance of these operations, a new algorithm to recognize actions of the equipment is developed. First, a video is represented as a collection of spatio-temporal features by extracting space-time interest points and describing each with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of these features by clustering their HOG descriptors. Equipment action categories are then learned using a multi-class binary Support Vector Machine (SVM) classifier. Given a novel video sequence, the proposed method recognizes and localizes equipment actions. The proposed method has been exhaustively tested on 859 videos from earthmoving operations. Experimental results with an average accuracy of 86.33% and 98.33% for excavator and truck action recognition respectively, reflect the promise of the proposed method for automated performance monitoring.
  • Thumbnail Image
    The field of human building interaction for convergent research and innovation for intelligent built environments
    Becerik-Gerber, Burcin; Lucas, Gale; Aryal, Ashrant; Awada, Mohamad; Berges, Mario; Billington, Sarah; Boric-Lubecke, Olga; Ghahramani, Ali; Heydarian, Arsalan; Hoelscher, Christoph; Jazizadeh, Farrokh; Khan, Azam; Langevin, Jared; Liu, Ruying; Marks, Frederick; Mauriello, Matthew Louis; Murnane, Elizabeth; Noh, Haeyoung; Pritoni, Marco; Roll, Shawn; Schaumann, Davide; Seyedrezaei, Mirmahdi; Taylor, John E.; Zhao, Jie; Zhu, Runhe (Nature Portfolio, 2022-12-21)
    Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.