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..
Learning Non-Uniform Hypergraph for Multi-Object Tracking
Authors: Longyin Wen, Dawei Du, Shengkun Li, Xiao Bian, Siwei Lyu8981-8988
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Several experiments are carried out on various challenging datasets (i.e., PETS09, Parking Lot sequence, Subway Face, and MOT16 benchmark), to demonstrate that our method achieves favorable performance against the state-of-the-art MOT methods. |
| Researcher Affiliation | Collaboration | 1JD Finance, Mountain View, CA, USA 2University at Albany, State University of New York, NY, USA 3GE Global Research, NY, USA |
| Pseudocode | Yes | Algorithm 1 Compute the local maximizer y |
| Open Source Code | Yes | The source code of the proposed method is available at https: //github.com/longyin880815. |
| Open Datasets | Yes | We conduct experiments on several popular MOT evaluation datasets, i.e., the multi-pedestrian tracking (Wen et al. 2016) (including the PETS09 and Parking Lot sequences), MOT2016 (Milan et al. 2016), and multi-face tracking (Wen et al. 2016) datasets |
| Dataset Splits | No | The paper mentions using a 'training set to learn the parameters' and 'testing set for evaluation', but does not explicitly describe a separate 'validation' dataset or split for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | To demonstrate the running time of NT, we run it five times using a single thread on a laptop with a 2.8 GHz Intel processor and 16 GB memory. |
| Software Dependencies | No | The paper states, 'We implement the NT algorithm in C++ without any code optimization,' but does not provide specific version numbers for C++ or any libraries used. |
| Experiment Setup | Yes | We set D = 4 in our experiments, and the learned weights of different degree of hyperedge are λ1 = 0.58535, λ2 = [0.15576, 3.0332, 0.34388], λ3 = 1.2879, and λ4 = 0.22324. The batch size τ in near-online tracking is set to 7. The minimal size of the sub-hypergraph is set as ˆα = 2. We fix all parameters to these values in the experiments. |