When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark
Authors: Xiao Wang, Jun Chen, Zheng Wang, Wu Liu, Shin'ichi Satoh, Chao Liang, Chia-Wen Lin
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: Night Surveillance. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of stateof-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of Night Surveillance. and 4 Experiments 4.1 Experiments Settings In this section, we compare the overall performance of classical pedestrian detection methods on the existing datasets and our dataset. |
| Researcher Affiliation | Collaboration | Xiao Wang1, Jun Chen 1, Zheng Wang 2, Wu Liu3, Shin ichi Satoh2, Chao Liang1, Chia-Wen Lin4 1Wuhan University 2National Institute of Informatics 3AI Research of JD.com 4National Tsing Hua University |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The paper provides a GitHub link in the abstract: 'https: //github.com/xiaowang1516/Night Surveillance' |
| Open Datasets | Yes | The paper introduces a new dataset, 'Night Surveillance,' and provides a GitHub link for its access in the abstract: 'https: //github.com/xiaowang1516/Night Surveillance' |
| Dataset Splits | No | The paper states, 'Then, we split the rest data into train and test portion according to the ratio of 1:1, and the overall distribution is shown in Table 1.' It provides details for training and test splits but does not mention a distinct validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or cloud instance specifications) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | No | The paper lists existing classical pedestrian detection methods that were evaluated but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or training configurations for these methods. |