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.