The human object segmentation and classification are main work in the applications of Intelligent Visual Surveillance
System or Passenger Flow Counting System. Traditional approaches to segment and classify human objects are usually
based on the face, leg motion and silhouette. These algorithms' performances and their applications have proved to be
effective in recent years. But these algorithms all assume that features can always be extracted. In complex situations,
however, features adopted in traditional algorithms might not be extracted, because human attitude and illumination
change greatly. In this case, if a definite feature is used, the algorithm's accuracy will fall. In this paper we propose an
approach to select the feature and the corresponding algorithm adaptively based on the human attitude and object
neighborhood illumination. The selected features can be used in the following tracking operation. Because this method
solves the human object segmentation and classification problem, it can broad the 3D recovery and behavior understanding
research results in simple situations to the application in complex situations.
In this paper, the algorithms are proposed for the human attitude and illumination detection, the feature selection strategies
in different situation are given. The experimental results show that the algorithm can detect the object lightness properly,
and can give the right attitude for feature selection. The algorithms have good performance and computation efficiency.