Integrating Occlusion and Illumination Modeling for Object Tracking Using Image Annotation

Abstract

Tracking occluded objects at different depths has become as extremely important component of study for any video sequence having wide applications in object tracking, scene recognition, coding, editing the videos and mosaicking. This paper experiments with the capabilities of image annotation contour based tracking for occluded object. Image annotation is applied on 3 similar normal video sequences varying in depth. In the experiment, one bike occludes the other at a depth of 60 cm, 80 cm and 100 cm respectively. The effect on tracking is also analyzed with illumination variations using 3 different light sources in video sequences having objects occluding one another at same depth. The paper finally studies the ability of annotation to track the occluded object based on pyramids with variation in depth further establishing a threshold at which the ability of the system to track the occluded object fails. The contour of both the individual objects cant be tracked due to the distortion caused by overlapping of the object pyramids. The thresholds established can be used as a bench mark to estimate the capability of different softwares. The paper further computes the frame by frame error incurred by the system, supported by detailed simulations. This system can be effectively used to achieve flawless tracking as the error in motion tracking can be corrected. This can be of great interest to computer scientists while designing surveillance systems etc.