Learning Non-Uniform Hypergraph for Multi-Object Tracking
Authors: Longyin Wen, Dawei Du, Shengkun Li, Xiao Bian, Siwei Lyu8981-8988
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |