| Model Type | Pose Estimation |
|---|---|
| Framework | TensorFlow, LiteRT (TensorFlow Lite), OpenVINO |
| Algorithm | PoseNet |
| Technical Details | Compatible with real-time inference pipelines and optimized for CPU/NPU acceleration on SECO platforms to estimate human body keypoints from a single RGB image. It supports multiple persons per frame and outputs skeleton data for each. Inspired by "PersonLab" (Papandreou et al.) and optimized for edge inference. |
PoseNet for Multi-Person Pose Detection
Lightweight and quantized PoseNet model for real-time multi-person pose estimation on edge hardware.
Leave your message here
Fill the form with the required information
$0.00
PoseNet for Multi-Person Pose Detection
app details
Resources
Use Cases
Industrial Safety
Surveillance
Supported SECO Devices
AI optimized hardware
Fanless embedded PCs
Modules
supported chipsets
Intel® Core™ i3
Qualcomm® Dragonwing QCS6490
Qualcomm® Dragonwing QCS5430
NXP i.MX 8M Plus
NXP i.MX 95
Performance Benchmark
Hardware
Latency (ms)
Memory Usage
Processing unit
SECO SOM-SMARC-QCS6490
208.57 ms
78.91 MB
CPU
SECO SOM-SMARC-QCS6490
13.85 ms
78.91 MB
NPU
SECO SOM SMARC iMX8Plus
1646 ms
78.85 MB
CPU
SECO SOM SMARC iMX8Plus
1204 ms
77 MB
NPU
SECO SOM-SMARC-MX95
754.16 ms
76.46 MB
CPU
SECO Titan 300 TGL-UP3 AI
78.02 ms
78.29 MB
CPU
Face Recognition for Industrial HMI
Secure face recognition HMI system for personalized user access in industrial environments.
View Model
People Counter
Lightweight real-time people counting model based on quantized YOLOX, optimized for edge devices using LiteRT (TensorFlow Lite) .
View Model
HRNet for Human Pose Estimation
High-resolution neural network for accurate keypoint detection in human pose estimation tasks.
View Model
Machine Alert Assistance with Operator Recognition
HMI system that uses AI to recognize operators during alerts and show personalized troubleshooting instructions.
View Model|
|