PANDA is the first gigaPixel-level humAN-centric video dataset, for large-scale, long-term, and multi-object visual analysis. The videos in PANDA were captured by a gigapixel camera and cover real-world large-scale scenes with both wide field-of-view (~1km^2 area) and high resolution details (~gigapixel-level/frame). The scenes may contain 4k head counts with over 100× scale variation. PANDA provides enriched and hierarchical ground-truth annotations, including 15,974.6k bounding boxes, 111.8k fine-grained attribute labels, 12.7ktrajectories, 2.2k groups and 2.9k interactions.

Data Annotation

File structure

For PANDA-Image, the training image and test image are stored in two compression packages respectively. The directory after the decompression contains the folders of each scene named after the scene name, each folder contains the pictures belonging to each scene.

For PANDA-Video, each video sequence is stored in a separate compression package. The compressed folder is named after the scene name and contains the frame images of the video sequences.

Annotation Formats

PANDA-Image

The two files human_bbox_train.jsonand vehicle_bbox_train.json respectively contain the annotations of the pedestrians and vehicles in the images for training set. human_bbox_test.json and vehicle_bbox_test.json only containimage_filepath, image id and image size for testing set. Please note that for the results on the test set to submit, the image id should be the same as in the annotation file.