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..
Online Multi-Object Tracking by Quadratic Pseudo-Boolean Optimization
Authors: Long Lan, Dacheng Tao, Chen Gong, Naiyang Guan, Zhigang Luo
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on publicly available datasets from both static and moving cameras demonstrate the superiority of our method. |
| Researcher Affiliation | Academia | College of Computer, National University of Defense Technology Centre for Quantum Computation & Intelligent Systems, FEIT, University of Technology, Sydney |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper mentions 'Our non-optimized codes run at around 3-12 fps' but does not provide any concrete access information, such as a specific repository link or an explicit code release statement, for the methodology described. |
| Open Datasets | Yes | Extensive experiments on publicly available datasets from both static and moving cameras demonstrate the superiority of our method. The datasets adopted here include PETS-S2L12, TUDCrossing, TUD-Campus [Andriluka et al., 2008], ETHbahnhof, and ETH-sunny [Ess et al., 2008]. 2http://www.cvg.reading.ac.uk/PETS2009/a.html |
| Dataset Splits | No | The paper uses terms like 'train' and 'validation' in a general context but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for its experiments. |
| Hardware Specification | Yes | We perform our experiments on a 3.45GHz PC with 6.0 GB memory with codes implemented in C++. |
| Software Dependencies | No | The paper mentions 'codes implemented in C++' but does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We set = 1 and β = 100 empirically. We set h = 100 and it has no obvious influence to the tracking performances. We use the deformable part-based detector [Felzenszwalb et al., 2010] to obtain hypotheses for each frame... Fj is the classifier score of Fj on di, we adopt the well-proven LBP and color features [Shu et al., 2012] in our appearance model and build the classifier similar to [Shu et al., 2012]. |