Sputnik Tracker: Having a Companion Improves Robustness of the Tracker

Authors:
Lukáš Cerman;Jiří Matas;Václav Hlaváč
Affiliations:
Faculty of Electrical Engineering, Center for Machine Perception, Czech Technical University, Prague 2, Czech Republic 121 35;Faculty of Electrical Engineering, Center for Machine Perception, Czech Technical University, Prague 2, Czech Republic 121 35;Faculty of Electrical Engineering, Center for Machine Perception, Czech Technical University, Prague 2, Czech Republic 121 35
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

Citing 11
Cited 1

Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Tracking with Bayesian Estimation of Dynamic Layer Representations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Mixture Framework for Motion Segmentation: Incorporating Spatial Coherence and Estimating the Number of Models

CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pictorial Structures for Object Recognition

International Journal of Computer Vision
Online Selection of Discriminative Tracking Features

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation

International Journal of Computer Vision
Tracking People by Learning Their Appearance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust tracking with motion estimation and local Kernel-based color modeling

Image and Vision Computing
Closed-world tracking of multiple interacting targets for indoor-sports applications

Computer Vision and Image Understanding
Robust online appearance models for visual tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

Dynamic context for tracking behind occlusions

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V

Quantified Score

Hi-index	0.00

Visualization

Abstract

Tracked objects rarely move alone. They are often temporarily accompanied by other objects undergoing similar motion. We propose a novel tracking algorithm called Sputnik Tracker. It is capable of identifying which image regions move coherently with the tracked object. This information is used to stabilize tracking in the presence of occlusions or fluctuations in the appearance of the tracked object, without the need to model its dynamics. In addition, Sputnik Tracker is based on a novel template tracker integrating foreground and background appearance cues. The time varying shape of the target is also estimated in each video frame, together with the target position. The time varying shape is used as another cue when estimating the target position in the next frame.