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Related Concept Videos

Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...

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Convolutional Neural Networks and Heuristic Methods for Crowd Counting: A Systematic Review.

Khouloud Ben Ali Hassen1, José J M Machado2, João Manuel R S Tavares2

  • 1Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal.

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Crowd counting is vital for public safety. While Convolutional Neural Networks (CNNs) dominate, heuristic models may outperform them in sparse crowd scenarios.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Safety Analytics

Background:

  • Crowd counting is essential for managing public spaces, aiding surveillance, safety, and planning.
  • Traditional methods have evolved, with a recent shift towards deep learning, particularly Convolutional Neural Networks (CNNs), due to their effectiveness.

Purpose of the Study:

  • To review and analyze crowd counting methods, focusing on the transition from heuristic to CNN-based approaches.
  • To compare the advantages and disadvantages of different crowd counting algorithms and architectures.
  • To categorize datasets into sparse and crowded, and evaluate method performance across them.

Main Methods:

  • Systematic analysis of algorithms and research in crowd counting.
  • Categorization of crowd counting methods into heuristic and CNN-based models.
  • Comparative evaluation of model performance on sparse and crowded datasets.

Main Results:

  • Identified a historical shift from heuristic to CNN models in crowd counting.
  • Analyzed differences, benefits, and drawbacks of various crowd counting techniques.
  • Found heuristic models can be more effective than CNNs in sparse crowd situations.

Conclusions:

  • The evolution of crowd counting methods shows a trend towards deep learning, but traditional approaches retain relevance.
  • Performance varies significantly based on crowd density, with heuristic models showing promise in sparse environments.
  • Further research can explore hybrid approaches or specialized CNNs for optimal performance across diverse crowd densities.