Data fusion for visual tracking with particles

By Patrick Pérez, Jaco Vermaak, Andrew Blake. Proceedings of the IEEE, 2004

540 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/dd5bc6c28c64197c32d93469a986d0100d392e7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ioannis A. Kakadiaris
2. Michalis Vrigkas
3. Christophoros Nikou
4. Xiaogang Wang
5. Jitendra Malik
6. Luc Van Gool
7. Xiaoou Tang
8. Bernt Schiele

In Media Technology

1. Ioannis A. Kakadiaris
2. Christophoros Nikou
3. Michalis Vrigkas
4. Xiaogang Wang

Citations in the corpus

• Fusion Techniques for Reliable Information: A Survey (2010, methodology): “For instance, the automated analysis based on VDF model faces severe challenges such as the lack of accurate statistical models for the signals and the high dimensionality for sampling rates at a low level [77].”
• Intelligent multi-camera video surveillance: A review (2013, methodology): “The Kalman filter (Mikic et al., 1998; Black et al., 2002), the extended Kalman filter (Straw et al., 2010) and the particle filter (Otsuka and Mukawa, 2004; Perez et al., 2004; Kim and Davis, 2006) are used to track objects in the 3D world coordinate system with occlusion handling.”
• A Review of Human Activity Recognition Methods (2015, methodology): “In that context, audio-visual analysis is used in many applications not only for audio-visual synchronization (Lichtenauer et al., 2011) but also for tracking (Perez et al., 2004) and activity recognition (Wu et al., 2013).”