MULTI-OBJECT FILTERING FROM IMAGE SEQUENCE WITHOUT DETECTION

ABSTRACT Almost every single-view visual multi-target tracking method presented in the literature includes a detection routine that maps the image data to point measurements relevant to the target states. These measurements are commonly further processed by a filter to estimate the number of targets and their states. This paper presents a novel visual tracking technique based on a multi-object filtering algorithm that operates directly on the image observations without the need for any detection. Experimental results on tracking sport players show that our proposed method can automatically track numerous interacting targets and quickly finds players entering or leaving the scene

MULTI-OBJECT FILTERING FROM IMAGE SEQUENCE WITHOUT DETECTION

ABSTRACT Almost every single-view visual multi-target tracking method presented in the literature includes a detection routine that maps the image data to point measurements relevant to the target states. These measurements are commonly further processed by a filter to estimate the number of targets and their states. This paper presents a novel visual tracking technique based on a multi-object filtering algorithm that operates directly on the image observations without the need for any detection. Experimental results on tracking sport players show that our proposed method can automatically track numerous interacting targets and quickly finds players entering or leaving the scene

State Estimation and Smoothing for the Probability Hypothesis Density Filter

Tracking multiple objects is a challenging problem for an automated system, with applications in many domains. Typically the system must be able to represent the posterior distribution of the state of the targets, using a recursive algorithm that takes information from noisy measurements. However, in many important cases the number of targets is also unknown, and has also to be estimated from data. The Probability Hypothesis Density (PHD) filter is an effective approach for this problem. The method uses a first-order moment approximation to develop a recursive algorithm for the optimal Bayesian filter. The PHD recursion can implemented in closed form in some restricted cases, and more generally using Sequential Monte Carlo (SMC) methods. The assumptions made in the PHD filter are appealing for computational reasons in real-time tracking implementations. These are only justifiable when the signal to noise ratio (SNR) of a single target is high enough that remediates the loss of information from the approximation. Although the original derivation of the PHD filter is based on functional expansions of belief-mass functions, it can also be developed by exploiting elementary constructions of Poisson processes. This thesis presents novel strategies for improving the Sequential Monte Carlo implementation of PHD filter using the point process approach. Firstly, we propose a post-processing state estimation step for the PHD filter, using Markov Chain Monte Carlo methods for mixture models. Secondly, we develop recursive Bayesian smoothing algorithms using the approximations of the filter backwards in time. The purpose of both strategies is to overcome the problems arising from the PHD filter assumptions. As a motivating example, we analyze the performance of the methods for the difficult problem of person tracking in crowded environment

Bayesian multi-object estimation from image observations

Analytic characterizations of the posterior distribution of a random finite set of states, conditioned on image observations are derived; under the assumption that the regions of the observation influenced by individual states do not overlap. These results provide tractable means to jointly estimate the number of states and their values in the Bayesian framework. As an application, we develop a multiobject filter suitable for image observations with low signal to noise ratio. A particle implementation of the multi-object filter is proposed and demonstrated via simulations.Ba-Ngu Vo, Ba-Tuong Vo, David Suter and Nam Trung Pha