Joint multitarget object tracking and interaction analysis by a probabilistic bio-inspired model

Authors:
Francesco Monti;Stefano Maludrottu;Carlo S. Regazzoni
Affiliations:
Department of Biophysical and Electronic Engineering, University of Genova, Italy., Genova, Italy;Department of Biophysical and Electronic Engineering, University of Genova, Italy., Genova, Italy;Department of Biophysical and Electronic Engineering, University of Genova, Italy., Genova, Italy
Venue:
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Year:
2010

Citing 11
Cited 0

A Particle Filter to Track Multiple Objects

WOMOT '01 Proceedings of the IEEE Workshop on Multi-Object Tracking (WOMOT'01)
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematical Techniques in Multisensor Data Fusion (Artech House Information Warfare Library)

Mathematical Techniques in Multisensor Data Fusion (Artech House Information Warfare Library)
Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Life Spans

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-camera Tracking and Atypical Motion Detection with Behavioral Maps

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Floor Fields for Tracking in High Density Crowd Scenes

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Dynamic tracking re-adjustment: a method for automatic tracking recovery in complex visual environments

Multimedia Tools and Applications
Machine learning for high-speed corner detection

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
An Efficient Sequential Approach to Tracking Multiple Objects Through Crowds for Real-Time Intelligent CCTV Systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a joint human tracking and human-to-human interaction recognition system is proposed. While usually these two functions are performed separately, it will be shown that it is possible to improve the tracking performances if these functions are done jointly. For this purpose, a Bayesian tracking algorithm is coupled with a bio-inspired interaction analysis framework. The motion patterns of moving entities provided by the tracker are analyzed in order to recognize the current situation; causal relationships between interacting individuals in the environment are formulated in terms of probabilistic distributions that are used to cue the tracker in closed loop. The effectiveness of the proposed approach is demonstrated for a variety of image sequences.