muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking
Authors: Congchao Wang, Yizhi Wang, Yinxue Wang, Chiung-Ting Wu, Guoqiang Yu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 {ccwang, yzwang, yxwang90, ctwu, yug}@vt.edu |
| 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. |