Lifted Disjoint Paths with Application in Multiple Object Tracking
Authors: Andrea Hornakova, Roberto Henschel, Bodo Rosenhahn, Paul Swoboda
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct several experiments on MOT showing the merit of using lifted disjoint paths for the tracking problem. |
| Researcher Affiliation | Academia | 1Computer Vision and Machine Learning, Max Planck Institute for Informatics, Saarbrücken, Saarland, Germany 2Institut for Image Processing, Leibniz University Hannover, Hannover, Niedersachsen, Germany. |
| Pseudocode | Yes | Algorithm 1 Separation for lifted path inequalities (10) ... Algorithm 2 Separation for lifted path-induced cut inequalities (11) and (12) ... Algorithm 3 Extract_path(P 1, v, w) |
| Open Source Code | Yes | Our code is available at https://github.com/ Andrea Hor/Lif T_Solver. |
| Open Datasets | Yes | We conduct extensive experiments on three challenging benchmarks: MOT15 (Leal Taixé et al., 2015), MOT16 and MOT17 (Milan et al., 2016), resulting in 39 test sequences. |
| Dataset Splits | Yes | We perform analysis and parameter tuning for our tracker on the MOT17 train set, even when our tracker is applied to the MOT15 sequences to ensure that our tracker is not prone to overfitting. We follow the MOT challenge protocol and use the detections provided by the respective benchmarks. All experiments on the training set are evaluated using a leave-one-out crossvalidation. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, or cloud computing instance types) are mentioned for the experimental setup within the provided text. |
| Software Dependencies | Yes | We solve the lifted disjoint paths problem (3) with the state of the art integer linear program solver Gurobi (Gurobi Optimization, 2019). |
| Experiment Setup | Yes | Input detections are rejected if Tracktor s detector outputs a confidence score σactive 0.5. Tracktor also applies a non-maxima-surpression on the reshaped input detections, where we use the threshold λnew = 0.6. |