SDD-Utils

A couple of utilities that can be used with Stanford Drone Dataset.

1. Create Pascal VOC style annotations

This utility helps to convert the videos and annotations in Stanford Drone Dataset into Pascal VOC style images and annotations.

The utility basically does three tasks in the following sequence:

• Split the video into frames
• Create Pascal VOC style annotation for each of the frame in the video
• Create train-validation-test split for the frames

This will come handy when we want to test existing Object Detection algorithms on Stanford Drone Dataset. This will enable us to train a network with minimal code change as most of the Object Detection algorithms do ship code to train on Pascal VOC.

Prerequisites

• ffmpeg library
• python 2.7

Usage

python annotate.py 

The script assumes that the Stanford Drone Datased is unzipped in the cwd of the script inside a folder named StanfordDroneDataset. A soft link will also work. The name of the folder is configurable in the script.

Declarative definition

The videos to be processed and the number of images in train-validation-test split can be defined declaratively in the script.

The videos_to_be_processed dictionary decides which videos should be processed and what would be its contribution towards train-validation-test set.

Key points:

• Keys in this dictionary should match the 'scenes' in Stanford Drone Dataset.
• Value for each key is also a dictionary. The number of items in this dictionary(child), can atmost be the number of videos each 'scene'.
• Each item in this dictionary(child) is of the form {video_number:fraction_of_images_to_be_split_into_train_val_test_set}

Example:

videos_to_be_processed = {'bookstore': {0: (.5, .2, .3), 1: (.2, .1, .1)},
                              'coupa': {0: (.5, .2, .3)}}

Here, the first two videos from bookstore scene from Stanford Drone Dataset will be split into frames, with a train-validation-test split of (.5, .2, .3) and (.2, .1, .1) respectively. Then the first video from coupa scene will be processed similarly.

Output

The Pascal VOC style annotations will be created in the location specified at destination_folder_name variable in the script. By default, it creates a folder named sdd inside the dataset folder.

Output Folder Structure:

cs17mtech01001@ubuntu:/media/sdj/Open_Datasets/StanfordDroneDataset/sdd$ ls
Annotations  ImageSets  JPEGImages  pickle_store

FAQ

1. What will happen when I run the script multiple time?

   Ans: If the videos specified in videos_to_be_processed dictionary is already made into frames, then those videos will not be again split. The train-validation-test set will be resampled for each run.

2. Suppose I need to have training and validation data from the first two videos of 'bookstore' scene and testing from the third video of 'deathCircle' scene, how would videos_to_be_processed dictionary look like?

videos_to_be_processed = {'bookstore': {0: (.5, .5, 0), 1: (.5, .5, 0)},
                        'deathCircle': {2: (0, 0, 1)}}

3. How many scenes are there in Stanford Drone Dataset? How many videos are there in each scene?

SDD contains the following 'scenes' and corresponding videos:
    'bookstore'   scene contains videos: (0, 1, 2, 3, 4, 5, 6)
    'coupa'       scene contains videos: (0, 1, 2, 3)
    'deathCircle' scene contains videos: (0, 1, 2, 3, 4)
    'gates'       scene contains videos: (0, 1, 2, 3, 4, 5, 6, 7, 8)
    'hyang'       scene contains videos: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
    'little'      scene contains videos: (0, 1, 2, 3)
    'nexus'       scene contains videos: (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
    'quad'        scene contains videos: (0, 1, 2, 3)

Citation

A. Robicquet, A. Sadeghian, A. Alahi, S. Savarese, Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes in European Conference on Computer Vision (ECCV), 2016.

Dataset is available here.