Automatic meeting analysis is an emerging research field. In this paper, we present stochastic algorithms for tracking people in multi-sensor meeting rooms, for a number of relevant tasks, including tracking multiple people, tracking head pose towards analysis of visual focus-of-attention, and tracking speaker activity using audio-visual information. A Bayesian framework based on Sequential Mon...

We present a system for automatic people tracking and activity recognition. This video includes the contribution of 3 papers over 2 years [1], [2], [3]. We show results on many sequences, including clips from a feature-length film and historical sports footage (all tracked completely automatically). We show results that utilize the tracker to obtain 3D reconstructions and activity descriptions ...

We propose in this paper a method for tracking groups of people in a metro scene to recognise abnormal behaviours such as violence or vandalism. After presenting the overall system, we describe the tracking algorithm for groups of people. Finally we present results illustrating the algorithm.

Particle filters are used for hidden state estimation with nonlinear dynamical systems. The inference of 3-d human motion is a natural application, given the nonlinear dynamics of the body and the nonlinear relation between states and image observations. However, the application of particle filters has been limited to cases where the number of state variables is relatively small, because the nu...

This paper describes the theory and implementation of a system of distributed sensors which work together to identify and track moving people using different sensing modalities in real time. Algorithms for detecting people using cameras and laser scanners are presented. A Kalman Filter is used to fuse the information gathered from the various sensors. Access to information from different kinds ...

We tackle multiple people tracking across multiple non-overlapping surveillance cameras installed in a wide area. Existing methods attempt to track people across cameras by utilizing appearance features and spatio-temporal cues to re-identify people across adjacent cameras. However, in relatively wide public areas like a shopping mall, since many people may walk and stay arbitrarily, the spatio...

In this paper we present a simplified tracking method to efficiently track people from a crowd video using the Minimum Mean Square Error (MMSE) technique. The system tracks people from a video sequence and is robust to varied lighting conditions and complex crowded scenes. The method effectively handles small occlusions and invokes a manual tracking procedure under severe occlusions. The system...

Tracking and modeling people from video sequences has become an increasingly important research topic, with applications including animation, surveillance and sports medicine. In this paper, we propose a model based 3–D approach to recovering both body shape and motion. It takes advantage of a sophisticated animation model to achieve both robustness and realism. Stereo sequences of people in mo...

This paper addresses the problem of detecting and tracking multiple moving people in a complex environment with unknown background. In this paper, we propose a new correlation-based matching technique for feature-based tracking. Our method was compared with two existing matching techniques, namely the normalized Euclidean distance and histogram-based matching. Experimental results on real-image...

The computer vision community has expended a great amount of effort in recent years towards the goal of tracking people in videos. Much more recently, algorithms have been developed to track multiple people in videos robustly and in real-time. The goal of this project is to implement a system based on one of those algorithms, in order to count and track the people in a database of surveillance ...