| The increasing use of surveillance cameras has generated a need of intelligent surveillance systems, which can identify the events of interest from the long video streams. Face detection is normally used for detecting and tracking people in the automated surveillance systems, but face detection is resource intensive and it is not a solved problem [13]. Soft biometric features like height and color have also been used for indoor surveillance cameras. During this thesis these soft biometric features are used for automation for surveillance systems in external environment where identifying and tracking a person is an added challenge due to shadow and occlusions with non-person objects. In this work, we first calibrate our fixed camera with respect to the world which is assumed to be planer. We then detect the moving pixels and blobs are constructed using contour tracing. Assuming each blob is a person, the height of blob is determined with the help of camera calibration parameters and the blob is divided into three parts, i-e, head, torso and legs. The color of each blob is estimated by averaging the R, G and B values of all the pixels in the block. Hence we have a set of four soft biometric features, height, color of head, color of torso and color of legs. These features are helpful when huge amount of videos are available in the database and the user has to find people in those videos that are six feet high and wearing a red shirt. The testing of the research is done on five videos in which three people of different height and wearing different clothes are walking. |
