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If you found our previous blog 'Ignitarium Releases Pre-trained AI Applications Library for Renesas RZ/V2L' interesting, we are back with the second release of pretrained AI libraries targeted for Renesas RZV2L device.

Ignitarium-Renesas Pretrained AI libraries Release-2

If you found our previous blog ‘Ignitarium Releases Pre-trained AI Applications Library for Renesas RZ/V2L‘ interesting, we are back with the second release of pretrained AI libraries targeted for Renesas RZV2L device. This release consists of new (and more complex!) applications that can readily run on the RZV2L device. Just like the previous set of applications, these applications are open source and free to use for everyone. 

Animal Detection Application 

Animal detection 1 photo

Animal detection 2 photo

This application leverages deep learning to detect an animal from the given input image or video stream from MIPI or USB camera. This application can detect the presence of 12 different animals i.e (Boar, Deer, Crow, Monkey, Bear, Racoon, Fox, Weasel, Skunk, Dog and cat). This application uses YoloV3 object detection network in the background. This application can help in wildlife Conversation, avoid animal accidents (early warning systems), intrusion alert and indoor/outdoor pet monitoring. 


Hand Gesture Recognition

Hand gesture recognition application recognizes a hand gesture made by single hand. The deep neural network used in the application identifies 21 key points on the palm. Then, these keypoints are processed using a classification algorithm to recognize the gesture.  9 different gestures such as one, two/scissor, three, four, five/paper, thumbs down, thumbs up and rock are recognized by the app. This application can act as a gesture based remote for devices. There are 2 versions of the same application in this release. One application uses hand keypoints to recognize the gesture, while the second application uses a scene-classification approach to do the same task. 


Human Gaze Detection 

This application refers to the process of identifying the direction of a person’s gaze or in simple words, where they are looking. Human gaze detection has multiple applications such as driver monitoring systems, Emotion recognition, customer analytics in retail space, digital signage and virtual and augmented reality. 


Driver Monitoring System

The driver monitoring system keeps track of the vehicle driver’s behavior and attentiveness on the road during driving. This is a very simple sample application that shows possibilities of a large scale, sophisticated system. The application can tell where the driver is looking at. (Center, Up, Down, Left and Right). The application can also detect eye blinking and yawning activity. This application can have much more complex enhancements like drowsiness detection, distraction detection, talking on phone, beverage drinking, talking to co-passenger etc. 


Head count from Top View 

Human Head counting application was there in first release. This newer application uses a model that is trained to detect human heads from the top view. This enhancement was done to address the video feeds from camera at public places which are mounted at the ceiling. This helps in occupancy detection at indoor public places, footfall detection, Integration with surveillance and tracking systems, security and monitoring, etc. This application has many challenges as there will be more occlusions with the head at crowded places, varying viewpoints, scale changes, low resolution inputs, etc. 

All these sample applications are developed using open-source deep learning models and open-source training data.  There are detailed instructions that allow developers to quickly evaluate the existing AI applications on RZ/V2L board as well as to experiment with other types of applications using these Deep Learning models.  Due to the inherent limitation of small datasets and state of DL models, the accuracy of the applications may be low. If there is a need to develop a productized version of any of these or other vision AI applications on RZ/V series of AI SoCs, please reach out to the teams at Ignitarium or Renesas, and we will enable the same.  

We are excited to see what interesting vision AI applications you would develop on RZ/V2L. Please post your queries using Issues or Discussions sections on GitHub, or write to us at info@ignitarium.com. We would also like to hear about new DL models and applications that you want to see supported on our roadmap.   

Stay tuned for more additions to the GitHub page.  

Ready Resources:  

RZ/V2L Overview and Documentation   

Ignitarium-Renesas RZ/V2L GitHub

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Human Pose Detection & Classification

Some Buildings in a city

Features:

  • Suitable for real time detection on edge devices
  • Detects human pose / key points and recognizes movement / behavior
  • Light weight deep learning models with good accuracy and performance

Target Markets:

  • Patient Monitoring in Hospitals
  • Surveillance
  • Sports/Exercise Pose Estimation
  • Retail Analytics

OCR / Pattern Recognition

Some Buildings in a city

Use cases :

  • Analog dial reading
  • Digital meter reading
  • Label recognition
  • Document OCR

Highlights :

  • Configurable for text or pattern recognition
  • Simultaneous Analog and Digital Dial reading
  • Lightweight implementation

Behavior Monitoring

Some Buildings in a city

Use cases :

  • Fall Detection
  • Social Distancing

Highlights :

  • Can define region of interest to monitor
  • Multi-subject monitoring
  • Multi-camera monitoring
  • Alarm triggers

Attire & PPE Detection

Some Buildings in a city

Use cases :

  • PPE Checks
  • Disallowed attire checks

Use cases :

  • Non-intrusive adherence checks
  • Customizable attire checks
  • Post-deployment trainable

 

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