Apperception: a database management system optimized for multi-video applications

Apperception ingests video data from many perspectives and makes them queryable as a single multidimensional visual object. It incorporates new techniques for optimizing, executing, and storing multi-perspective video data.

How to Setup Apperception Repo

Install dependencies:

apt-get update && apt-get install -y postgresql python3-opencv

Clone the Apperception repo

For ssh:

git clone git@github.com:apperception-db/apperception.git
cd apperception

For HTTPS:

git clone https://github.com/apperception-db/apperception.git
cd apperception

Downloading Official YOLOv4 Pre-trained in the repo

Copy and paste yolov4.weights from your downloads folder into this repository. For the Demo, we use yolov4-tiny.weights,

If you want to use yolov4-tiny.weights, a smaller model that is faster at running detections but less accurate, download file here: https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.weights

Our object tracker uses YOLOv4 to make the object detections, which deep sort then uses to track. There exists an official pre-trained YOLOv4 object detector model that is able to detect 80 classes. For easy demo purposes we will use the pre-trained weights for our tracker. Download pre-trained yolov4.weights file: https://drive.google.com/open?id=1cewMfusmPjYWbrnuJRuKhPMwRe_b9PaT

Download the yolov4 model, unzip in the current repo https://drive.google.com/file/d/1g5D0pU-PKoe7uTHI7cRjFlGRm-xRQ1ZL/view?usp=sharing

Then we setup the repo

chmod u+x ./setup.sh
chmod 733 ./setup.sh
./setup.sh

Apperception Demo Tryout without TASM

As TASM requires nividia-docker/nvidia-docker2(https://www.ibm.com/docs/en/maximo-vi/8.2.0?topic=planning-installing-docker-nvidia-docker2) during runtime, and a machine with an encode-capable GPU (https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new). To tryout Apperception features without TASM, run the following:

Start Apperception Metadata Store MobilityDB(https://github.com/MobilityDB/MobilityDB)

docker volume create mobilitydb_data
docker run --name "mobilitydb" -d -p 25432:5432 -v mobilitydb_data:/var/lib/postgresql mobilitydb/mobilitydb

We need to setup the mobilitydb with customized functions

cd pg_extender
psql -h localhost -p 25432 -d mobilitydb -U docker
Enter "docker" as the default password
\i overlap.sql;
\q

Try the demo.

In apperception repo: jupyter notebook or python3 -m notebook

The demo notebook first constructs the world. Then it queries for the trajectory of the cars that appeared once in an area of interests within some time interval.

To fully activate apperception in TASM:

docker-compose up
cd pg_extender
psql -h 172.19.0.3 -d mobilitydb -U docker
Enter "docker" as the default password
\i overlap.sql
\q
docker ps

After fetching the CONTAINER_ID of apperceptiontasm/tasm:latest, run

docker exec -it {CONTAINER_ID of apperceptiontasm/tasm:latest} /bin/bash

Now we are under TASM env

cd /apperception/
pip3 install -r requirements.txt

Try the demo.

In the docker:
jupyter notebook --ip 172.19.0.2 --port 8890 --allow-root & Directly open the jupyter url The demo notebook first constructs the world. Then it queries for the trajectory and videos of the cars that appeared once in an area of interests within some time interval.