WALKING WALKing walking: Action Recognition from Action Echoes
Authors: Qianli Ma, Lifeng Shen, Enhuan Chen, Shuai Tian, Jiabing Wang, Garrison W. Cottrell
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | achieves state-of-the-art performance on four skeleton benchmark data sets. We evaluate our proposed Conv ESN on four skeleton-based action recognition benchmarks |
| Researcher Affiliation | Academia | School of Computer Science and Engineering, South China University of Technology, Guangzhou, China Department of Computer Science and Engineering, University of California, San Diego, CA, USA |
| Pseudocode | No | The paper presents mathematical equations and diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate our proposed Conv ESN on four skeleton-based action recognition benchmarks: MSR-Action 3D (MSRA3D). [Li et al., 2010], the Motion Capture Dataset HDM05 [M uller et al., 2007], the Florence3DAction [Seidenari et al., 2013] and the UTKinect-Action dataset [Xia et al., 2012]. |
| Dataset Splits | Yes | We use a standard validation protocol used by [Li et al., 2010] on the MSR-Action 3D dataset. In this protocol, we split the whole dataset into three overlapping subsets (AS1, AS2, AS3) of 8 classes for each one. Within each set, we adopt cross-subject validation: the subjects 1, 3, 5, 7, 9 are used for training and 2, 4, 6, 8, 10 are used for testing. Following the protocol used in previous work [Du et al., 2015], we perform 10-fold cross validation on this dataset. |
| Hardware Specification | Yes | The machine setup is on an Intel Core i5-6500, 3.20GHz CPU 32-GB RAM and a Ge Force GTX 980-Ti 6G. |
| Software Dependencies | No | we use the back-propagation algorithm and the gradient optimization method ADAM [Kingma and Ba, 2014] to optimize parameters in Conv ESN. No specific version numbers for ADAM or other software libraries are provided. |
| Experiment Setup | Yes | Implementation Details. The IS of reservoir is 0.1, the Sr is 0.99, and the reservoir sizes range 100 to 300. For the multiscale convolutional layer, we choose the width of sliding windows as 2, 3, & 4 and the number of filters under each width ranges from 16 to 128. The size of the final fusion layer is set as 144. Preprocessing is an important step for action recognition... We normalize the coordinate system... We then apply the popular Savitzky-Golay smoothing filter [Steinier et al., 1972] to smooth the joint trajectories... To reduce computational cost on the HDM05 dataset we sample every 4 frames... Finally, for variable length trajectories, we pad them with zeros up to a given max-length value. |