This is an application that takes JSONLines formatted video label dataset as input and builds context dependent knowledge base.

kivy for visualization of frames and recognition results

Ensure you have the latest pip, wheel, and virtualenv:

$python -m pip install --upgrade pip wheel setuptools virtualenv

Optionally create a new virtual environment for your Kivy project. Highly recommended:

First create the environment named kivy_venv in your current directory:

$python -m virtualenv kivy_venv

Activate the virtual environment or on windows CMD do:

$kivy_venv\Scripts\activate

Install the dependencies and kivy

$python -m pip install docutils pygments pypiwin32 kivy.deps.sdl2 kivy.deps.glew

Given the following video frames at 3 [FPS]

• E01_S01

Input data must be JSONLines that contains multiple JSON objects in one of the formats below.

Object

• {"type": "object", "class": "person", "label": "Person A", "seconds": 15.0, "coordinates": [100, 200, 20, 30]}
• {"type": "object", "id": "person_ross_geller", "class": "person", "label": "Ross Geller", "seconds": 15.0, "coordinates": [100, 200, 20, 30]}
• https://github.com/vtt-project/DRCaptioning

Behavior

• {"type": "behavior", "class": "stand", "seconds": 15.0, "object": Object}
• https://github.com/vtt-project/vtt-action-recognition

Emotion

• {"type": "emotion", "class": "happy", "seconds": 15.0, "object": Object}
• https://github.com/vtt-project/VTT_vid_emotion

Relation

• {"type": "relation", "class": RelationalClass, "subclass": "wear", "seconds": 15.0, "source": Object, "target": Object}
• https://github.com/vtt-project/vtt-char-identify

Place

• {"type": "location", "class": "central_perk", "seconds": 15.0}
• https://github.com/vtt-project/2nd-year

Sound event

• {"type": "sound", "class": "glass_crashing", "seconds": 15.0}
• https://github.com/vtt-project/vtt-sound-event-detection

Person re-id

• {"id": "person_ross_geller"} or
• {"coordinates": [100, 200, 20, 30]}
• https://github.com/vtt-project/VTT_TRACKING

RelationalClass

• "behavior" for subclass "wear", "hold", "hug", …
• "emotion" for subclass "love", "like", …
• "position" for subclass "above", "below", "next_to", …
• "social" for subclass "son_of", "father_of", "lover_of", "friend_of", …

kivyVideoOpencvLabel.py takes input data from several modules and visualize the results on video frames.

$ python kivyVideoOpencvLabel.py

main.py takes input data as standard input, and prints result knowledge base as standard output as a JSONLines formatted string. For example:

video-knowledge-builder $ python main.py < test_input.jsonlines > output.jsonlines

The shell command above will provide the contents of test_input.jsonlines as input for the application and save the result in a file output.jsonlines.

This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2017-0-01780, The technology development for event recognition/relational reasoning and learning knowledge based system for video understanding)