Moving shadow detection via binocular vision and colour clustering

Abstract

A pedestrian segmentation algorithm in the presence of cast shadows is presented in this study. The novelty of this algorithm lies in the fusion of multi‐view and multi‐plane homographic projections of foregrounds and the use of the fused data to guide colour clustering. This brings about an advantage over the existing binocular algorithms in that it can remove cast shadows while keeping pedestrians’ body parts, which occlude shadows. Phantom detection, which is inherent with the binocular method, is also investigated. Experimental results with real‐world videos have demonstrated the efficiency of this algorithm.

Improved Method for Positioning Crane Grab Boom Corner Points using Hough Transform and K-means Clustering

“…The coordinate points mapped to the Hough space correspond to a straight line in the image coordinate system, Crane grabbing boom has 4 edge lines. Therefore, the clustering method [20][21] is adopted to cluster all the line parameter points detected by the Hough transform into the same number of classes as the number of edges of Crane grabbing boom . Compared to other clustering algorithms, the k-menas algorithm has advantages such as simple algorithm idea, fast convergence speed, better clustering effect, and the main parameter that needs to be adjusted is only the number of clusters K. When other clustering algorithms are applied to edge maps with significant interference, they will cluster the coordinates of the line parameter points detected by Hough transform into an uncertain number of clusters.…”