Tracking Appearances with Occlusions

نویسندگان

  • Ying Wu
  • Ting Yu
  • Gang Hua
چکیده

In Proc. of IEEE Conf. on CVPR’03, Madison, Wisconsin, 2003 Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during tracking. To cope with the occlusion problem explicitly, this paper proposes a dynamic Bayesian network which accommodates an extra hidden process for occlusion and stipulates the conditions on which the image observation likelihood is calculated. The statistical inference of such a hidden process can reveal the occlusion relations among different targets, which makes the tracker more robust against partial even complete occlusions. In addition, considering the fact that target appearances change with views, another generative model for multiple view representation is proposed by adding a switching variable to select from different view templates. The integration of the occlusion model and multiple view model results in a complex dynamic Bayesian network, where extra hidden processes describe the switch of targets’ templates, the targets’ dynamics, and the occlusions among different targets. The tracking and inferencing algorithms are implemented by the sampling-based sequential Monte Carlo strategies. Our experiments show the effectiveness of the proposed probabilistic models and the algorithms.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking

Object localization and tracking are key issues in the analysis of scenes for video surveillance or scene understanding applications. This paper presents a contribution to the object tracking task in indoor environments surveyed by multiple fixed cameras. The method proposed uses a foreground separation process at each camera view. Then, a 3Dforeground scene is modeled and discretized into voxe...

متن کامل

An Efficient Target Tracking Algorithm Based on Particle Filter and Genetic Algorithm

In this paper, we propose an efficient hybrid Particle Filter (PF) algorithm for video tracking by employing a genetic algorithm to solve the sample impoverishment problem. In the presented method, the object to be tracked is selected by a rectangular window inside which a few numbers of particles are scattered. The particles’ weights are calculated based on the similarity between feature vecto...

متن کامل

Transductive inference for color-based particle filter tracking

Robust real−time tracking of non−rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this type of tracking problem because of its invariance in the presence of changing complex shapes and appearances. To track the color object, a particle filter uses several hypotheses simultaneously and weights them by thei...

متن کامل

Capturing and Recognizing Objects Appearance Employing Eigenspace

This paper presents a method of capturing objects appearances from its environment and it also describes how to recognize unknown appearances creating an eigenspace. This representation and recognition can be done automatically taking objects various appearances by using robotic vision from a defined environment. This technique also allows extracting objects from some sort of complicated scenes...

متن کامل

POD: Colour detection of people and objects for multi-camera video tracking

Visual tracking of humans has proved to be an extremely challenging task for computer vision systems. Often the colour appearance of a person can provide enough information to identify an object or person in the short-term. However, the imprecise nature of colour measurements typically encountered in image processing has limited their use. This paper presents a system which uses the colour appe...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2003