Online Multi-Object Tracking by Quadratic Pseudo-Boolean Optimization
Authors: Long Lan, Dacheng Tao, Chen Gong, Naiyang Guan, Zhigang Luo
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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]. |