Multiple hypothesis tracking for multiple target tracking

By S. Blackman. IEEE Aerospace and Electronic Systems Magazine, 2004

1,482 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/71255ceae7c78200e9c37675b39c7d3436771e6f

How researchers see this paper

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

In Artificial Intelligence

1. B. Vo
2. Wing-Kin Ma
3. Hao Zhu
4. Henry Leung
5. Sergio A. Velastin
6. Paolo Remagnino
7. Ka-Veng Yuen
8. Lyudmila S. Mihaylova

In Cognitive Neuroscience

1. Hao Zhu
2. Henry Leung
3. Wing-Kin Ma
4. B. Vo
5. Ka-Veng Yuen
6. Lyudmila S. Mihaylova

In Electrical and Electronic Engineering

1. Hao Zhu
2. Henry Leung
3. Wing-Kin Ma
4. B. Vo
5. Ka-Veng Yuen
6. Lyudmila S. Mihaylova
7. Derrick Wing Kwan Ng

Citations in the corpus

• Model based vehicle detection and tracking for autonomous urban driving (2009, neutral): “Blackman et al. (2004) provides a summary.”
• Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “Methods for explicit data association tracking vary widely: from the nearest neighbor (NN) algorithm [127], the multihypothesis tracking (MHT) approach [128], the probabilistic data association (PDA) approach [129], to the joint probability data association (JPDA) algorithms [130], [131].”
• The Gaussian Mixture Probability Hypothesis Density Filter (2006, background): “An intrinsic problem in multiple-target tracking is the unknown association of measurements with appropriate targets [1], [2], [6], [7].”
• Crowd analysis: a survey (2008, background): “A details summary and a discussion of MHT for multiple target tracking is included in [13].”