A Complete Variational Tracker
Authors: Ryan D Turner, Steven Bottone, Bhargav Avasarala
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the applicability of our method on radar tracking and computer vision problems. We use the VS-PETS 2003 soccer player data set as a real data example to validate our method. |
| Researcher Affiliation | Industry | Ryan Turner Northrop Grumman Corp. ryan.turner@ngc.com Steven Bottone Northrop Grumman Corp. steven.bottone@ngc.com Bhargav Avasarala Northrop Grumman Corp. bhargav.avasarala@ngc.com |
| Pseudocode | No | The paper describes algorithms such as 'variational algorithm' and 'loopy belief propagation', and provides mathematical derivations of steps, but it does not include a dedicated pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | No | The paper does not contain any explicit statement about making the source code available or provide a link to a code repository. |
| Open Datasets | Yes | We borrow the radar tracking example of the OMGP paper [18]." and "We use the VS-PETS 2003 soccer player data set as a real data example to validate our method. ... Soccer data source: http://www.cvg.rdg.ac.uk/slides/pets.html. |
| Dataset Splits | No | The paper states, 'The parameters for the NCV, R, PD, λ, and the track meta-state parameters were trained by optimizing the variational lower bound Lβ on the first 1000 frames...' and 'We split the remainder of the data into 70 sequences of K = 20 frames for a test set.' This indicates training and testing sets, but no explicit validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as library or solver names, that are needed to replicate the experiment. |
| Experiment Setup | Yes | We borrow the radar tracking example... We have made the example more realistic by adding clutter λ = 8 and missed detections PD = 0.5". For the soccer example: "The parameters for the NCV, R, PD, λ, and the track meta-state parameters were trained by optimizing the variational lower bound Lβ on the first 1000 frames". |