Group Cost-Sensitive Boosting for Multi-Resolution Pedestrian Detection
Authors: Chao Zhu, Yuxin Peng
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is evaluated on the challenging Caltech pedestrian benchmark, and outperforms other state-of-the-art on different resolution-specific test sets. |
| Researcher Affiliation | Academia | Chao Zhu and Yuxin Peng Institute of Computer Science and Technology, Peking University Beijing 100871, China {zhuchao, pengyuxin}@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 Group Cost-Sensitive Ada Boost |
| Open Source Code | No | The paper mentions evaluation software but does not provide access to the source code for the methodology described in this paper. |
| Open Datasets | Yes | The experiments are conducted on the Caltech pedestrian detection benchmark (Dollár et al. 2012) |
| Dataset Splits | Yes | The pedestrian detector is trained on the training set (set00-set05), and the detection results are reported on the test set (set06-set10). ... The optimal value of the costs for different groups are selected from Cfp = 1, Cfnh [1 : 0.1 : 10] and Cfnl [Cfnh : 0.1 : Cfnh + 10] by cross-validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (like CPU/GPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using LDCF detector and Ada Boost but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | The optimal value of the costs for different groups are selected from Cfp = 1, Cfnh [1 : 0.1 : 10] and Cfnl [Cfnh : 0.1 : Cfnh + 10] by cross-validation. 4096 weak classifiers are trained and combined to a strong classifier... Three bootstrapping stages are applied with 25,000 additional hard negatives each time. |