Journal of Theoretical and Applied Information Technology 10 th May 2014. Vol. 63 No.1 © 2005 - 2014 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 104 NONRETINOTOPIC PARTICLE FILTER FOR VISUAL TRACKING 1 ALEXANDER A S GUNAWAN, 2 ITO WASITO 1 Bina Nusantara University, Mathematics Department, School of Computer Science, Jakarta, Indonesia 2 Universitas Indonesia, Faculty of Computer Science, Depok, Indonesia E-mail: 1 1, 2 ito.wasito@cs.ui.ac.id ABSTRACT Visual tracking is the problem of using visual sensor measurements to determine location and path of target object. One of big challenges for visual tracking is full occlusion. When full occlusions are present, image data alone can be unreliable, and is not sufficient to detect the target object. The developed tracking algorithm is based on bootstrap particle filter and using color feature target. Furthermore the algorithm is modified using nonretinotopic concept, inspired from the way of human visual cortex handles occlusion by constructing nonretinotopic layers. We interpreted the concept by using past tracking memory about motion dynamics rather than current measurement when quality level of tracking reliability below a threshold. Using experiments, we found (i) the performance of the object tracking algorithm in handling occlusion can be improved using nonretinotopic concept, (ii) dynamic model is crucial for object tracking, especially when the target object experienced occlusion and maneuver motions, (iii) the dependency of the tracker performance on the accuracy of tracking quality threshold when facing illumination challenge. Preliminary experimental results are provided. Keywords: Visual Tracking, Full Occlusion, Bootstrap Particle Filter, Color, Nonretinotopic, Tracking Quality Threshold 1. INTRODUCTION In this research, we consider the problem of tracking single object in video sequences using only one camera. In particular, we focus on the cases where target object occludes by other objects, either partially or fully. Partial occlusion hides some parts of the target while complete occlusion hides the entire target for some time. Many techniques exist to handle the occlusion problem with particle filter probabilistic models, such as in [1]. But the proposed tracking method uses network of many cameras to handle this problem. The other proposed tracking method [2] adds the adaptiveness of likelihood function and invariance of color distributions to particle filtering. Note that the target object can be rigid (e.g., car) or deformable (e.g., person). The main question of the research is how to track object which overcome full occlusion in within finite period time. Inspired by human visual perception which shows that the representation of the visual cortex occurs in a nonretinotopic manner [3], we developed nonretinotopic particle filter. Visual processing is often assumed to be retinotopic, which means the visual process where object in the environment are projected to photo- receptors in the retina in a similar manner as appearance models in a digital image. Nevertheless, a recent study on human vision [3] shows that the representation in higher visual areas of the visual cortex occurs in a nonretinotopic approach: visual perception seems to create dynamic layers for each moving object in the scene. This representation suggests that the appearance of the objects and their positions are marginal independent. The nonretinotopic approach describes our visual processing that always maintains the identity of observed objects across space and time [4]. The implementation of visual tracking algorithm in this research is based on Bayesian framework, mainly bootstrap particle filter. Furthermore the algorithm is modified using nonretinotopic concepts. To evaluate and compare the capabilities of two tracking algorithms: generic and retinotopic particle filter, we use various video sequences and analyze the tracking performance. The paper is organized as follows: first we discuss the Bayesian framework and its implementation based on bootstrap particle filter. For observation model in experiments, it is used color feature of target object. Then, we consider the nonretinotopic concept and