Abstract
Abstract: Video log images could potentially be used for state transportation agencies to automatically inventory traffic sign assets. However, processing millions of video log images is prohibitively time‐consuming. Taking advantage of the emerging chip multicore processor (CMP) technology, this article proposes a generalized framework for parallelizing traffic sign detection in a large number of high‐resolution video log images. Based on an improved contour finding and workload identification strategy, task and data parallelism in traffic sign detection are fully developed at multiple levels. A generalized parallelization framework for dynamic workload scheduling using adaptive work‐stealing of thread pool and dynamic circular lock‐free double‐ended queue is then proposed. Experimental results on 14,514 images provided by the Louisiana Department of Transportation show that the parallelized traffic sign detection algorithm has great potential to improve computation time with a parallel speedup of up to 18 times on multilevel parallel configurations and different CMP platforms while keeping the same accuracy as the serial version.
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