Abstract:
A major challenge for object tracking system is to detect object due to occlusion. Here we
describe method to address partial occlusions for human detection in still images. For that
random subspace method (RSM) is chosen for building a classifier ensemble robust
against partial occlusions. The component classifiers are chosen on the basis of their
individual and combined performance. The main contribution of this work lies in our
approach’s capability to improve the detection rate when partial occlusions are present
without compromising the detection performance on non-occluded data. In contrast to
many recent approaches, a method which does not require manual labeling of body parts,
defining any semantic spatial components, or using additional data coming from motion
or stereo. Moreover, the method can be easily extended to other object classes. The
different approaches are evaluated at the classification and detection levels for both
partially occluded and non-occluded data. There are some challenges like to detect
human occlusion with skin color background and to detect human occlusion in complex
image with existing system and we use different detection technique to detect human
occlusion in such conditions.