Concrete column recognition in images and videos

The automated detection of structural elements (e.g., columns and beams) from visual data can be used to facilitate many construction and maintenance applications. The research in this area is under initial investigation. The existing methods solely rely on color and texture information, which makes them unable to identify each structural element if these elements connect each other and are made of the same material. The paper presents a novel method of automated concrete column detection from visual data. The method overcomes the limitation by combining columns’ boundary information with their color and texture cues. It starts from recognizing long vertical lines in an image/video frame through edge detection and Hough transform. The bounding rectangle for each pair of lines is then constructed. When the rectangle resembles the shape of a column and the color and texture contained in the pair of lines are matched with one of the concrete samples in knowledge base, a concrete column surface is assumed to be located. This way, one concrete column in images/videos is detected. The method was tested using real images/videos. The results are compared with the manual detection ones to indicate the method’s validity.

Step synchronization and third person speed perception in virtual environment locomotion simulators

Human locomotion is known to be influenced by observation of another person's gait. For example, athletes often synchronize their step in long distance races. However, how interaction with a virtual runner affects the gait of a real runner has not been studied. We investigated this by creating an illusion of running behind a virtual model (VM) using a treadmill and large screen virtual environment showing a video of a VM. We looked at step synchronization between the real and virtual runner and at the role of the step frequency (SF) in the real runner's perception of VM speed. We found that subjects match VM SF when asked to match VM speed with their own (Figure 1). This indicates step synchronization may be a strategy of speed matching or speed perception. Subjects chose higher speeds when VMSF was higher (though VM was 12km/h in all videos). This effect was more pronounced when the speed estimate was rated verbally while standing still. (Figure 2). This may due to correlated physical activity affecting the perception of VM speed [Jacobs et al. 2005]; or step synchronization altering the subjects' perception of self speed [Durgin et al. 2007]. Our findings indicate that third person activity in a collaborative virtual locomotive environment can have a pronounced effect on an observer's gait activity and their perceptual judgments of the activity of others: the SF of others (virtual or real) can potentially influence one's perception of self speed and lead to changes in speed and SF. A better understanding of the underlying mechanisms would support the design of more compelling virtual trainers and may be instructive for competitive athletics in the real world. © 2009 ACM.

Improvements to Concrete Column Detection in Live Video

The automated detection of structural elements (e.g. concrete columns) in visual data is useful in many construction and maintenance applications. The research in this area is under initial investigation. The authors previously presented a concrete column detection method that utilized boundary and color information as detection cues. However, the method is sensitive to parameter selection, which reduces its ability to robustly detect concrete columns in live videos. Compared against the previous method, the new method presented in this paper reduces the reliance of parameter settings mainly in three aspects. First, edges are located using color information. Secondly, the orientation information of edge points is considered in constructing column boundaries. Thirdly, an artificial neural network for concrete material classification is developed to replace concrete sample matching. The method is tested using live videos, and results are compared with the results obtained with the previous method to demonstrate the new method improvements.