View independent vehicle/person classification
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A Texture Based Shoe Retrieval System for Shoe Marks of Real Crime Scenes
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Social signal processing: Survey of an emerging domain
Image and Vision Computing
Human detection in indoor environments using multiple visual cues and a mobile robot
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A texture recognition system of real shoe marks taken from crime scenes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Human tracking using multiple-camera-based head appearance modeling
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Human distribution estimation using shape projection model based on multiple-viewpoint observations
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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We propose a statistical method to detect human(s) in images by using geometrical structures common to the appearances of the target objects (human figures). Most appearance-based methods focus on pixel values directly because the same classes of objects usually have similar pixel value distributions. However, this is not true for some particular objects. Humans are a good example. Human figures have a variety of different clothes, and their pixel values (color, brightness) can vary significantly from person to person. In this case, geometrical structures observed as pixel value distances are essential for the successful recognition of objects. In this paper, we propose a method to describe and recognize the appearances of objects based on geometrical structures. The representation is based on a statistical analysis of Mahalanobis distances among parts of images. Using our method, objects having pixel value variety can be recognized using a small number of appearance models. Experimental results for human figures demonstrate the effectiveness of our method.