Action2Activity: Recognizing Complex Activities from Sensor Data
Authors: Ye Liu, Liqiang Nie, Lei Han, Luming Zhang, David S. Rosenblum
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a real-world dataset demonstrate the effectiveness of our work. |
| Researcher Affiliation | Academia | School of Computing, National University of Singapore Department of Computer Science, Hong Kong Baptist University |
| Pseudocode | No | The paper describes algorithms textually and mathematically but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using LIBSVM and OpenCV, providing links to these third-party libraries, but does not state that the authors' own implementation code for the described methodology is publicly available. |
| Open Datasets | Yes | The Opportunity dataset [Chavarriaga et al., 2013] |
| Dataset Splits | Yes | The performance reported in this paper was measured based on 10-fold cross-validation classification accuracy. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | We implemented this method with the help of LIBSVM2. We selected a linear kernel. ... We employed the k-Nearest Neighbors in Open CV3 and set K = 7. |
| Experiment Setup | Yes | For a MTL, we set minsup = 0.01 and twin = 2 Lavg over all the experiments, where Lavg is the average length of action intervals in an activity. ... We initially fixed λ and θ, and then varied γ from 0.001 to 5 and doubled the value at each step. ... We then set γ = 0.001, θ = 1 and varied λ. ... Finally, we set γ = 0.001, λ = 0.05 and varied θ. |