This paper presents a comprehensive framework for tracking coarse human models from sequences of synchronized monocular grayscale images in multiple camera coordinates. It demonstrates the feasibility of an end-to-end person tracking system using a unique combination of motion analysis on 3D geometry in different camera coordinates and other existing techniques in motion detection, segmentation, and pattern recognition. The system starts with tracking from a single camera view. When the system predicts that the active camera will no longer have a good view of the subject of interest, tracking will be switched to another camera which provides a better view and requires the least switching to continue tracking. The nonrigidity of the human body is addressed by matching points of the middle line of the human image, spatially and temporally, using Bayesian classification schemes. Multivariate normal distributions are employed to model class-conditional densities of the features for tracking, such as location, intensity, and geometric features. Limited degrees of occlusion are tolerated within the system. Experimental results using a prototype system are presented and the performance of the algorithm is evaluated to demonstrate its feasibility for real time applications.

This paper presents a comprehensive framework for tracking moving humans in an indoor environment from sequences of synchronized monocular grayscale images captured from multiple fixed cameras. The proposed framework consists of three main modules: Single View Tracking (SVT), Multiple View Transition Tracking (MVTT), and Automatic Camera Switching (ACS). Bayesian classification schemes based on motion analysis of human features are used to track (spatially and temporally) a subject image of interest between consecutive frames. The automatic camera switching module predicts the position of the subject along a spatial-temporal domain, and then, selects the camera which provides the best view and requires the least switching to continue tracking. Limited degrees of occlusion are tolerated within the system. Tracking is based upon the images of upper human, bodies captured from various viewing angles, and non-human moving objects are excluded using Principal Component Analysis (PCA). Experimental results are presented to evaluate the performance of the tracking system.

Presents a framework for tracking human motion in an indoor environment from sequences of monocular grayscale images obtained from multiple fixed cameras. Multivariate Gaussian models are applied to find the most likely matches of human subjects between consecutive frames taken by cameras mounted in various locations. Experimental results from real data show the robustness of the algorithm and its potential for real time applications.

This paper presents an approach to tracking human motion in a sequence of monocular images. The process consists of detecting motion, segmenting moving subjects by recovering the background and, finally, tracking the subject of interest. The usual assumptions of small image motion, a fixed viewing system and constant velocity are systematically relaxed. Two cases are studied: (1) a viewing system with negligible motion, and (2) a viewing system with non-negligible motion.