A new fuzzy based algorithm for solving stereo vagueness in detecting and tracking people
This paper describes a system capable of detecting and tracking various people using a new approach based on color, stereo vision and fuzzy logic. Initially, in the people detection phase, two fuzzy systems are used to filter out false positives of a face detector. Then, in the tracking phase, a new fuzzy logic based particle filter (FLPF) is proposed to fuse stereo and color information assigning different confidence levels to each of these information sources. Information regarding depth and occlusion is used to create these confidence levels. This way, the system is able to keep track of people, in the reference camera image, even when either stereo information or color information is confusing or not reliable. To carry out the tracking, the new FLPF is used, so that several particles are generated while several fuzzy systems compute the possibility that some of the generated particles correspond to the new position of people. Our technique outperforms two well known tracking approaches, one based on the method from Nummiaro et al. [1] and other based on the Kalman/meanshift tracker method in Comaniciu and Ramesh [2]. All these approaches were tested using several color-with-distance sequences simulating real life scenarios. The results show that our system is able to keep track of people in most of the situations where other trackers fail, as well as to determine the size of their projections in the camera image. In addition, the method is fast enough for real time applications. (C) 2011 Elsevier Inc. All rights reserved.
stereo vision fuzzy logic people detection particle filtering color information people tracking