EvDownsampling dataset

This dataset is used in the publication "EvDownsampling: A Robust Method For Downsampling Event Camera Data", ECCV Workshop on Neuromorphic Vision: Advantages and Applications of Event Cameras [29/09/2024].This dataset contains event streams of highly dynamic real-world scenes collected using two DVS cameras of different spatial resolutions – a DVXplorer (640×480 px) and a Davis346 (346×260 px). Both cameras simultaneously recorded each scene with negligible parallax error. The dataset is provided to test event-based spatio-temporal downsampling techniques through comparing downsampled higher-resolution recordings with matching lower-resolution recordings, as explained in our publication above.There are four classes {class_folder} of scenes:Traffic: natural lighting. Bus and car moving across camera visual field with several pedestrians. 6 seconds long.HandGestures: fluorescent lighting. Person either waving their hand, waving their arms or doing jumping jacks. 12-15 seconds long.Corridor: fluorescent lighting. Moving through corridors. One corridor scene (Pevensey) has a carpet which provides texture, while the other scene (Arundel) does not have a carpet. 18-24 seconds long.Cars: natural lighting. Car moving across camera visual field with few pedestrians. 3-5 seconds long.Each dataset/{class_folder} contains two folders consisting of:Videos of the scene recordings captured by both DVS cameras placed side-by-side (.mp4)Raw event data information in the form of (x, y, timestamp, polarity) in AEDAT 4 format (.aedat4).The script dualCam_dvRead.py can be used to convert the .aedat4 files into a NumPy format and to generate <i>frame</i> reconstructions. The syntax to call the script from the command-line is:python3 dualCam_dvRead.py --data_folder {class_folder} --input {scene_recording} --publisher_rate {publisher_rate}class_folder is the class of the scene recording e.g. corridorscene_recording is the specific recording in that class e.g. Pevenseypublisher_rate determines frame rate of images published (in fps) e.g. 1000.More information is available at: https://github.com/anindyaghosh/EvDownsampling.The conference website is: https://sites.google.com/view/nevi2024/home-page.

本数据集用于发表于论文《EvDownsampling：一种鲁棒的事件相机数据下采样方法》的研究，该论文收录于2024年9月29日举办的ECCV神经形态视觉研讨会：事件相机的优势与应用（Neuromorphic Vision: Advantages and Applications of Event Cameras）。
本数据集包含由两台不同空间分辨率的动态视觉传感器（Dynamic Vision Sensor, DVS）采集的高动态真实场景事件流——分别为DVXplorer（640×480像素）与Davis346（346×260像素）。两台相机同步采集各场景，视差误差可忽略不计。本数据集旨在通过对比下采样后的高分辨率录制结果与匹配的低分辨率录制结果，测试基于事件的时空下采样技术，具体细节可参见上述论文。

数据集包含四类场景，分别对应{class_folder}类文件夹：
1. 交通（Traffic）：自然光照环境下，公交车与汽车横穿相机视场，伴随多名行人，录制时长6秒。
2. 手部动作（HandGestures）：荧光照明环境下，受试者做出挥手、挥臂或开合跳动作，录制时长12-15秒。
3. 走廊（Corridor）：荧光照明环境下的走廊穿行场景。其中一个走廊场景（Pevensey）铺设带有纹理的地毯，另一场景（Arundel）未铺设地毯，录制时长18-24秒。
4. 车辆（Cars）：自然光照环境下，汽车横穿相机视场，伴随少量行人，录制时长3-5秒。

每个dataset/{class_folder}路径下包含两个子文件夹：
- 两台并排摆放的DVS相机录制的场景视频（.mp4格式）
- 以AEDAT 4格式（AEDAT 4 Format）存储的原始事件数据，数据格式为(x, y, 时间戳, 极性)。

可使用dualCam_dvRead.py脚本将.aedat4文件转换为NumPy格式，并生成帧（frame）重建结果。命令行调用语法如下：
python3 dualCam_dvRead.py --data_folder {class_folder} --input {scene_recording} --publisher_rate {publisher_rate}
其中，class_folder为场景录制的类别（例如corridor），scene_recording为该类别下的具体录制场景（例如Pevensey），publisher_rate用于指定生成图像的帧率（单位：fps，即帧每秒），例如1000。

更多详情可访问：https://github.com/anindyaghosh/EvDownsampling。本次研讨会官网为：https://sites.google.com/view/nevi2024/home-page。