Scalable Solution - This human upper-body detection and counting demo utilizes Lattice’s ECP5 or CrossLink-NX FPGAs and their Convolutional Neural Network (CNN) acceleration engines.
Highly Customizable - This demo, based on the object counting reference design, is provided with end to end components to allow for modification of the object being detected.
Rapid Implementation - The demo utilizes popular Lattice’s Development Kit, which provide all components from image sensor to output to host system.
Features
- Accelerated, low-power human presence detection and counting using neural network model
- VGG, Mobilenetv1, Mobilenetv2, Resnet and SSD type structures are supported
- TF Lite based implementation for ease of use
- Reference designs are provided to enable design replication and transfer learning
- Total power consumption of ECP5 FPGA is 0.85 W and CrossLink-NX less than 200mW
- Processing at up to 60 FPS and VGA resolution
