Particle swarm optimization and differential evolution for model-based object detection

Roberto Ugolotti*, Youssef S.G. Nashed, Pablo Mesejo, Spela Ivekovic, Luca Mussi, Stefano Cagnoni

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


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 nVIDIATM CUDA computing architecture.

Original languageEnglish (US)
Pages (from-to)3092-3105
Number of pages14
JournalApplied Soft Computing Journal
Issue number6
StatePublished - 2013


  • Articulated models
  • Deformable models
  • Differential Evolution
  • Global continuous optimization
  • Object detection
  • Particle Swarm Optimization
  • Pose estimation

ASJC Scopus subject areas

  • Software


Dive into the research topics of 'Particle swarm optimization and differential evolution for model-based object detection'. Together they form a unique fingerprint.

Cite this