GPU accelerated one-pass algorithm for computing minimal rectangles of connected components

Abstract

The connected component labeling is an essential task for detecting moving objects and tracking them in video surveillance application. Since tracking algorithms are designed for real-time applications, efficiencies of the underlying algorithms become critical. In this paper we present a new one-pass algorithm for computing minimal binding rectangles of all the connected components of background foreground segmented video frames (binary data) using GPU accelerator. The given image frame is scanned once in raster scan mode and the background foreground transition information is stored in a directed-graph where each transition is represented by a node. This data structure contains the locations of object edges in every row, and it is used to detect connected components in the image and extract its main features, e.g. bounding box size and location, location of the centroid, real size, etc. Further we use GPU acceleration to speed up feature extraction from the image to a directed graph from which minimal bounding rectangles will be computed subsequently. Also we compare the performance of GPU acceleration (using Tesla C2050 accelerator card) with the performance of multi-core (up 24 cores) general purpose CPU implementation of the algorithm.