Tracking human motion using multiple cameras

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Tracking Human Motion Using Multiple Cameras



Q. Cai and J. K. Aggarwal

Computer and Vision Research Center

Department of Electrical and Computer Engineering

The University of Texas at Austin

email: aggarwaljk@mail.utexas.edu

Abstract

This paper presents a framework for tracking human mo-

tion in an indoor environment from sequences of monoc-

ular grayscale images obtained from multiple ﬁxed cam-

eras. Multivariate Gaussian models are applied to ﬁnd the

most likely matches of human subjects between consecutive

frames taken by cameras mounted in various locations. Ex-

perimental results from real data show the robustness of the

algorithm and its potential for real time applications.

1. Introduction

Tracking human motion in an indoor environment is of

interest in applications of surveillance. In particular, we

are developing a methodology to track individuals at sites

such as corridors, airports, borders, and secured buildings.

This requires that the viewing system be able to image the

tracked subject in a broad area over a long period of time.

In pursuit of this goal, our work has evolved from study-

ing human walking using a ﬁxed camera [1, 2] to tracking

non-backgroundobjects in a single moving camera [3]. The

studies in tracking using a ﬁxed single camera [4, 2, 5] are

limited to a very narrow area due to the restricted viewing

angle of the system. A moving camera with a substantial

degree of rotational freedom [3] increases the viewing angle

to certain degree, however, it complicates the implementa-

tion by adding the motion estimation of both the viewing

system and the subject of interest, and is still limited in

the amount of viewing area. In this work, we chose to use

multiple ﬁxed cameras mounted in the area of interest to

track and monitor the motion of individuals in sequences

of monocular grayscale images. As long as the subject is

within the area monitored by the ﬁxed cameras, the image

of this subject will be contained in the view of at least one

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The research reported in this paper was supported in part by the Army

Research Ofﬁce, Contract DAAH-94-G-0417 and Texas Advanced Tech-

nology Program, Contract ATP-442.

camera. Based on this scenario, the problem of monitoring

a subject becomes that of tracking the subject of interest in

one camera view and matching that subject across different

camera views, where the cameras’ intrinsic parameters and

relative positions are assumed to be known a priori.

To establish correspondencebetween consecutiveframes

from different cameras, conventional tracking methods

based on the similarity of the object shape, such as cross-

correlation and line-edge matching, are not applicable be-

cause the shape of an object image varies drastically from

view to view of different cameras, and the whole body of a

moving human usually goes through complicated changes

during motion. Deformable template tracking for non-rigid

objects does not ﬁt either because the contours of the human

shape are not always complete in cluttered indoor scenes. In

addition, the continuity of the motion ﬂow does not retain

in the views of multiple cameras. Optical ﬂow methods [6],

which are widely used for featureless motion tracking, de-

mandsmall and smoothmotion between frames,arestriction

that also does not hold in our case. In this paper,we propose

to track a moving human in different camera views based on

low level recognition of human motion [5]. A simpler form

of a 2D human model [7] is applied to detect movinghuman

subjects. Tracking between consecutive frames is mainly

based on the consistency of the position, velocity, and av-

erage intensity of feature points formulated by multivariate

Gaussian models, considered in the views of various cam-

eras. The proposed algorithm is computationally efﬁcient

and can be readily used in real time applications.

2. Pre-processing

Three stages of pre-processing are performed before

tracking begins: 1) segmentation of the non-background

objects from the still background, 2) detection of human

subjects from the segmented non-background objects, and

3) feature extraction from the segmented human subjects.

The quality of object segmentation plays a critical role in

later processing. If an non-background object is missed at

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