Particle Swarm Optimization and Differential Evolution for model-based object detection

  • Authors:

  • Roberto Ugolotti;Youssef S. G. Nashed;Pablo Mesejo;ŠPela Ivekovič;Luca Mussi;Stefano Cagnoni

  • Affiliations:

  • Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy;Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy;Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy;Department of Mechanical & Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK;Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy and Henesis s.r.l., Viale dei Mille 108, 43125 Parma, Italy;Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2013

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Abstract

Automatically detecting objects in images or video sequences is one of the most relevant and frequently tackled tasks in computer vision and pattern recognition. The starting point for this work is a very general model-based approach to object detection. The problem is turned into a global continuous optimization one: given a parametric model of the object to be detected within an image, a function is maximized, which represents the similarity between the model and a region of the image under investigation. In particular, in this work, the optimization problem is tackled using Particle Swarm Optimization (PSO) and Differential Evolution (DE). We compare the performances of these optimization techniques on two real-world paradigmatic problems, onto which many other real-world object detection problems can be mapped: hippocampus localization in histological images and human body pose estimation in video sequences. In the former, a 2D deformable model of a section of the hippocampus is fit to the corresponding region of a histological image, to accurately localize such a structure and analyze gene expression in specific sub-regions. In the latter, an articulated 3D model of a human body is matched against a set of images of a human performing some action, taken from different perspectives, to estimate the subject's posture in space. Given the significant computational burden imposed by this approach, we implemented PSO and DE as parallel algorithms within the nVIDIA(TM) CUDA computing architecture.