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..
Pose-Assisted Multi-Camera Collaboration for Active Object Tracking
Authors: Jing Li, Jing Xu, Fangwei Zhong, Xiangyu Kong, Yu Qiao, Yizhou Wang759-766
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results demonstrate that our system outperforms all the baselines and is capable of generalizing to unseen environments. |
| Researcher Affiliation | Collaboration | 1Center for Data Science, Peking University 2Computer Science Dept., Sch l of EECS, Peking University 3Advanced Innovation Center for Future Visual Entertainment(AICFVE), Beijing Film Academy 4Key Lab. of System Control and Information Processing (Mo E), Shanghai; Automation Dept., Shanghai Jiao Tong University 5Center on Frontiers of Computing Studies, Peking University 6Deepwise AI Lab ... This work was supported by MOST-2018AAA0102004, NSFC-61625201, NSFC61527804, Qualcomm University Research Grant. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The code and demo videos are available on our website https://sites.google.com/view/pose-assistedcollaboration. |
| Open Datasets | Yes | Specifically, we choose pictures from a texture dataset (Kylberg 2011) and place them on the surface of walls, floor, obstacles etc. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components and algorithms (e.g., A3C algorithm, Conv-LSTM network, GRU, CNNs, LSTM Network) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The action space is discrete and contains eleven candidate actions (turn left, turn right, turn up, turn down, turn topleft, turn top-right, turn bottom-left, turn bottom-right, zoom in, zoom out and keep still). We take a two-phase training strategy for learning. Specifically, we choose pictures from a texture dataset (Kylberg 2011) and place them on the surface of walls, floor, obstacles etc. And we apply the A3C algorithm to update the network architecture of the Pose-Assisted Multi-Camera Collaboration System. |