People count application With Deepstream SDK and Transfer Learning Toolkit

People count application With Deepstream SDK and Transfer Learning Toolkit

Git repo: https://github.com/AIoT-IST/deepstream-occupancy-analytics/tree/master

Description

This is a sample application for counting people entering/leaving in a building using NVIDIA Deepstream SDK, Transfer Learning Toolkit (TLT) and pre-trained models. This application can be used to build real-time occupancy analytics application for smart buildings, hospitals, retail, etc. The application is based on deepstream-test5 sample application.

It can take streaming video or Neon camera as input, counts the number of people crossing a tripwire. In this application, you will learn:

  • How to use PeopleNet model from NGC
  • How to use NvDsAnalytics plugin to draw line and count people crossing the line

You can extend this application to change region of interest, use cloud-to-edge messaging to trigger record in the DeepStream application or build analytic dashboard or database to store the metadata.

To learn how to build this demo step-by-step, check out the on-demand webinar on Creating Intelligent places using DeepStream SDK.

Prerequisites

Getting Started

  • Preferably clone the repo in /opt/nvidia/deepstream/deepstream-6.3/sources/apps/sample_apps

    sudo apt-get update && sudo apt-get upgrade -y
sudo apt-get install libjson-glib-dev libgstrtspserver-1.0-dev -y
sudo chmod 777 -R /opt/nvidia/deepstream/deepstream-6.3/
git clone https://github.com/AIoT-IST/deepstream-occupancy-analytics.git /opt/nvidia/deepstream/deepstream-6.3/sources/apps/sample_apps/deepstream-occupancy-analytics/

  • Download peoplnet model:

    cd /opt/nvidia/deepstream/deepstream-6.3/sources/apps/sample_apps/deepstream-occupancy-analytics/config && ./model.sh

  • For Jetson use: bin/jetson/libnvds_msgconv.so

Build and Configure

  • Set CUDA_VER in the MakeFile as per platform.

For Jetson, CUDA_VER=11.4

  cd /opt/nvidia/deepstream/deepstream-6.3/sources/apps/sample_apps/deepstream-occupancy-analytics && make

Run

  cd /opt/nvidia/deepstream/deepstream-6.3/sources/apps/sample_apps/deepstream-occupancy-analytics/
  ./deepstream-test5-analytics -c config/dstest_occupancy_analytics.txt

Modify the boarder

  cd /opt/nvidia/deepstream/deepstream-6.3/sources/apps/sample_apps/deepstream-occupancy-analytics/tool
  python3 preview.py

Output

The output will look like this:

alt-text

Where you can see the kafka messages for entry and exit count.

References