Hidden Follower Detection: How Is the Gaze-Spacing Pattern Embodied in Frequency Domain?
Authors: Shu Li, Ruimin Hu, Suhui Li, Liang Liao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the feature fidelity of time series and HFD performance are positively correlated, and the fidelity of frequencydomain features and HFD performance are significantly better than the time-domain features. On both real and simulated datasets, the accuracy of the proposed method is increased by 3%, and the gaze-only module is improved by 10%. |
| Researcher Affiliation | Academia | 1School of Cyber Engineering, Xidian University 2School of Computer Science and Engineering, Nanyang Technological University |
| Pseudocode | No | The paper describes methods and calculations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release, or mention of code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | Real-HFD. ... More details can be found in (Xu et al. 2021, 2022). |
| Dataset Splits | Yes | We trained with 4-fold cross-validation, of which the ratio of the training set and test set in both datasets is 7:3. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software tools and models like Tra De S, Dense Pose, Gaze360, and TWIESN, but does not specify their version numbers or other software dependencies with specific versions. |
| Experiment Setup | Yes | The optimal gaze and spacing MFCC parameters in Fig. 1 are: gaze MFCCs with dimension = 4, w = 10s, overlap = 2s and spacing MFCCs with dimension = 4, w = 8s, overlap = 1s. ... If P>0.6, the pedestrian is recognized as a hidden follower. ... For the time-domain features, we try to reconstruct using an autoregressive model: Transformer, we keep the network structure, set b L as the outputting reconstructed series, and introduce the SDR as the loss function. |