Fundamentals of interactive computer graphics
Fundamentals of interactive computer graphics
Stereo Error Detection, Correction, and Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Analysis of Stereo Quantization Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Integrated Person Tracking Using Stereo, Color, and Pattern Detection
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maintaining Multi-Modality through Mixture Tracking
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-person tracker with a fixed slanting stereo camera
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
People detection and tracking through stereo vision for human-robot interaction
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Sequential Monte Carlo methods for multiple target tracking anddata fusion
IEEE Transactions on Signal Processing
Video security for ambient intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiple and variable target visual tracking for video-surveillance applications
Pattern Recognition Letters
Multi-cue-based CamShift guided particle filter tracking
Expert Systems with Applications: An International Journal
GPGPU implementation of growing neural gas: Application to 3D scene reconstruction
Journal of Parallel and Distributed Computing
Radar-based road-traffic monitoring in urban environments
Digital Signal Processing
Hi-index | 0.10 |
Multiple object tracking is a difficult task, especifically when there is not an explicit model of the object being tracked or when it is not possible to estimate the background of the scene. This paper proposes a novel approach for multiple target tracking. It works without background information and uses an original method that merges colour and depth information. The fusion of both pieces of information is created taking into account a confidence measure about the depth information. The method proposed employs a multiple particle filter approach in which particle weights are modified by an interaction factor in order to avoid the ''coalescence'' problem. In addition, the method performs as a pure colour-based technique when no disparity information is available, and takes advantage of depth information to enhance tracking whenever it is possible. Our technique is compared with two pure colour-based tracking approaches (the particle filtering method proposed by Nummiaro et al. [Nummiaro, K., Koller-Meier, E., Van Gool, L., 2003. An adaptive color-based particle filter. Image and Vision Computing, 21, 99-110] and the Kalman/mean-shift tracker [Comaniciu, D., Ramesh, V. 2000. Mean shift and optimal prediction for efficient object tracking. In: IEEE International Conference on Image Processing (ICIP'00), vol. 3, pp. 70-73]) and a pure stereo-based approach derived from our problem formulation. The performance of the four algorithms is tested using several colour-with-depth sequences of images showing different coloured targets in complex situations. The results show that our proposal is able to track the targets in case of complex backgrounds and to properly determine the size of their projections in the camera image (while the other methods fail). Besides, the proposed method is fast enough for real-time applications and the use of 3D information helps to track several targets simultaneously without confusing their identities.