Human Detection Using a Mobile Platform and Novel Features Derived From a Visual Saliency Mechanism

Human Detection Using a Mobile Platform and Novel Features Derived From a Visual Saliency Mechanism. Montabone, S. & Soto, A. Image and Vision Computing, 28(3):391-402, 2010.

Paper abstract bibtex 4 downloads

Human detection is a key ability to an increasing number of applications that operates in human inhab- ited environments or needs to interact with a human user. Currently, most successful approaches to human detection are based on background substraction techniques that apply only to the case of static cameras or cameras with highly constrained motions. Furthermore, many applications rely on features derived from specific human poses, such as systems based on features derived from the human face which is only visible when a person is facing the detecting camera. In this work, we present a new com- puter vision algorithm designed to operate with moving cameras and to detect humans in different poses under partial or complete view of the human body. We follow a standard pattern recognition approach based on four main steps: (i) preprocessing to achieve color constancy and stereo pair calibration, (ii) seg- mentation using depth continuity information, (iii) feature extraction based on visual saliency, and (iv) classification using a neural network. The main novelty of our approach lies in the feature extraction step, where we propose novel features derived from a visual saliency mechanism. In contrast to previous works, we do not use a pyramidal decomposition to run the saliency algorithm, but we implement this at the original image resolution using the so-called integral image. Our results indicate that our method: (i) outperforms state-of-the-art techniques for human detection based on face detectors, (ii) outperforms state-of-the-art techniques for complete human body detection based on different set of visual features, and (iii) operates in real time onboard a mobile platform, such as a mobile robot (15 fps).

@Article{	  montabone:etal:2010,
  author	= {S. Montabone and A. Soto},
  title		= {Human Detection Using a Mobile Platform and Novel Features
		  Derived From a Visual Saliency Mechanism},
  journal	= {Image and Vision Computing},
  volume	= {28},
  number	= {3},
  pages		= {391-402},
  year		= {2010},
  abstract	= {Human detection is a key ability to an increasing number
		  of applications that operates in human inhab- ited
		  environments or needs to interact with a human user.
		  Currently, most successful approaches to human detection
		  are based on background substraction techniques that apply
		  only to the case of static cameras or cameras with highly
		  constrained motions. Furthermore, many applications rely on
		  features derived from specific human poses, such as systems
		  based on features derived from the human face which is only
		  visible when a person is facing the detecting camera. In
		  this work, we present a new com- puter vision algorithm
		  designed to operate with moving cameras and to detect
		  humans in different poses under partial or complete view of
		  the human body. We follow a standard pattern recognition
		  approach based on four main steps: (i) preprocessing to
		  achieve color constancy and stereo pair calibration, (ii)
		  seg- mentation using depth continuity information, (iii)
		  feature extraction based on visual saliency, and (iv)
		  classification using a neural network. The main novelty of
		  our approach lies in the feature extraction step, where we
		  propose novel features derived from a visual saliency
		  mechanism. In contrast to previous works, we do not use a
		  pyramidal decomposition to run the saliency algorithm, but
		  we implement this at the original image resolution using
		  the so-called integral image. Our results indicate that our
		  method: (i) outperforms state-of-the-art techniques for
		  human detection based on face detectors, (ii) outperforms
		  state-of-the-art techniques for complete human body
		  detection based on different set of visual features, and
		  (iii) operates in real time onboard a mobile platform, such
		  as a mobile robot (15 fps). },
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/ImageVisionComp-10.pdf}
}

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