mohamedmaghrby / openvinoinference Goto Github PK

The E2E sample implements aspects of smart city sensing, analytics and management features as follows:

• Camera Provisioning: Tag and calibrate cameras for installation locations, calibration parameters and other usage pattern information.
• Camera Discovery: Discover and register IP cameras on specified IP blocks. Registered cameras automatically participate into the analytics activities. See Sensor Simulation and Discovery for additional details.
• Recording: Record and manage segmented camera footage for preview or review (at a later time) purpose.
• Analytics: Perform analytics on the live/recorded camera streams. Latency-sensitive analytics are performed on Edge while others are on cloud.
• Triggers and Alerts: Manage triggers on analytics data. Respond with actions on triggered alerts.
• Smart Upload and Archive: Transcode and upload only critical data to cloud for archival or further offline analysis.
• Stats: Calculate statistics for planning/monitoring purpose on analytical data.
• UI: Present above data to users/administrators/city planners.

The sample implements the Smart-City traffic and stadium scenarios. The traffic scenario measures vehicle/pedestrian flow for planning purpose. The stadium scenario focuses on different access control techniques, including entrance people counting, service-point queue counting, and stadium seating zone crowd counting.

Traffic	Stadium

The sample is powered by the following Open Visual Cloud software stacks:

• Edge Low-latency Analytics:
  • The GStreamer-based media analytics stack is used for object detection, people-counting, queue-counting and crowd-counting on camera streams. The software stack is optimized for Intel® Xeon® Scalable Processors and Intel VCAC-A.

• Smart Upload with Transcoding:
  • The FFmpeg-based media transcoding stack is used to transcode recorded content before uploading to cloud. The software stack is optimized for Intel Xeon Scalable Processors.

• Time Zone: Check that the timezone setting of your host machine is correctly configured. Timezone is used during build. If you plan to run the sample on a cluster of machines managed by Docker Swarm or Kubernetes, please make sure to synchronize time among the manager/master node and worker nodes.

• Build Tools: Install cmake, m4 and gawk if they are not available on your system.

• Docker Engine:

  • Install docker engine. Make sure you setup docker to run as a regular user.
  • Setup docker swarm, if you plan to deploy through docker swarm. See Docker Swarm Setup for additional setup details.
  • Setup Kubernetes, if you plan to deploy through Kubernetes. See Kubernetes Setup for additional setup details.
  • Setup docker proxy as follows if you are behind a firewall:

sudo mkdir -p /etc/systemd/system/docker.service.d       
printf "[Service]\nEnvironment=\"HTTPS_PROXY=$https_proxy\" \"NO_PROXY=$no_proxy\"\n" | sudo tee /etc/systemd/system/docker.service.d/proxy.conf       
sudo systemctl daemon-reload          
sudo systemctl restart docker     

Use the following commands to build the sample. By default, the sample builds to the traffic scenario. To enable the stadium scenario, use cmake -DSCENARIO=stadium ... See also: Build Options.

mkdir build    
cd build     
cmake ..    
make     

Use the following commands to start/stop services via docker swarm:

make update # optional for private registry
make start_docker_swarm      
make stop_docker_swarm      

See also: Docker Swarm Setup.

Use the following commands to start/stop Kubernetes services:

make update # optional for private registry
make start_kubernetes
make stop_kubernetes

See also: Kubernetes Setup.

Launch your browser and browse to https://<hostname> for the sample UI.

• For Kubernetes/Docker Swarm, <hostname> is the hostname of the manager/master node.
• If you see a browser warning of self-signed certificate, please accept it to proceed to the sample UI.

• Configuration Options
• Docker Swarm Setup
• Kubernetes Setup
• Intel VCAC-A Setup