Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
Authors: Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted three sets of experiments for the detailed analysis of efficiency improvement. |
| Researcher Affiliation | Academia | Department of Electrical and Computer Engineering, Virginia Tech EMAIL |
| Pseudocode | No | The paper provides flowcharts and diagrams to illustrate the algorithm, but no explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We selected four public datasets including three natural image MOT datasets (ETHZ (BAHNHOF and JELMOLI) [8], KITTI-Car [11], MOT CVPR 2019 Challenge[6]) and one particle tracking dataset (ISBI12 Particle Tracking Challenge (PTC) [5]). |
| Dataset Splits | No | The paper mentions using public datasets and detection results from them but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All the experiments were based on... on a single core of 2.40GHz Xeon(R) CPU E5-2630. |
| Software Dependencies | No | The paper mentions that 'SSP and our mu SSP were implemented in C++' and 'Follow Me was re-implemented from their python package in C++', but it does not specify any version numbers for compilers, libraries, or other software dependencies. |
| Experiment Setup | Yes | The graph has 5866 vertices and 36688 edges, and the algorithm runs totally 400 iterations. |