In this paper we describe a system that enables a mobile robot equipped with a color vision system to track humans in indoor environments. We developed a method for tracking humans when they are within the field of view of the camera, based on motion and color cues. However, the robot also has to keep track of humans which leave the field of view and re-enter later. We developed a dynamic Bayesian network for such a global tracking task. Experimental results on real data confirm the viability of the developed method.

Keeping Track of Humans: Have I Seen This Person Before?

As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth’s surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road traffic monitoring. In this article, we address the problem of fast object tracking in satellite videos, by developing a novel tracking algorithm based on correlation filters embedded with motion estimations. Based on the kernelized correlation filter (KCF), the proposed algorithm provides the following improvements: 1) proposing a novel motion estimation (ME) algorithm by combining the Kalman filter and motion trajectory averaging and mitigating the boundary effects of KCF by using this ME algorithm and 2) solving the problem of tracking failure when a moving object is partially or completely occluded. The experimental results demonstrate that our algorithm can track the moving object in satellite videos with 95% accuracy.

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Object Tracking in Satellite Videos by Improved Correlation Filters With Motion Estimations

This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses.

/pdf/online-updating-appearance-generative-mixture-model-for-47muyktp3c.pdf

Online updating appearance generative mixture model for meanshift tracking

The Manus manipulator is a wheelchair mounted assistive device for severely motor disabled persons. It is a six degrees of freedom rehabilitation robot (excluding external lift and gripper) of which the control can be improved through the introduction of sensors, especially for users with very limited rest functionality. The approach presented in this paper combines sensor based control with direct user control resulting in a collaborative controller and deals with human interface extensions and improvements.

Collaborative control of the MANUS manipulator

This paper presents a novel feature set for visual tracking that is derived from "oriented energies". More specifically, energy measures are used to capture a target's multiscale orientation structure across both space and time, yielding a rich description of its spatiotemporal characteristics. To illustrate utility with respect to a particular tracking mechanism, we show how to instantiate oriented energy features efficiently within the mean shift estimator. Empirical evaluations of the resulting algorithm illustrate that it excels in certain important situations, such as tracking in clutter with multiple similarly colored objects and environments with changing illumination. Many trackers fail when presented with these types of challenging video sequences.

Spatiotemporal oriented energy features for visual tracking

In this paper we present a generative probabilistic model of the appearance of a nonrigid object and an iterative procedure for searching for the maximum likelihood (ML) estimate of the position and shape of the tracked object in a new image. The shape of the object in an image is approximated by an ellipse that is described by a full covariance matrix. The appearance of the object is described by a color-histogram. The algorithm is used for tracking persons in image sequences.

B.J.A. Kröse

Papers

A probabilistic model for an EM-like object tracking algorithm using color histograms