This demo showcases Pedestrian Tracking scenario: it reads frames from an input video sequence, detects pedestrians in the frames, and builds trajectories of movement of the pedestrians in a frame-by-frame manner. You can use a set of the following pre-trained models with the demo:
person-detection-retail-0013, which is the primary detection network for finding pedestrians
person-reidentification-retail-0031, which is the network that is executed on top of the results from inference of the first network and makes reidentification of the pedestrians
On June 2019 Raspberry pi announce new version of raspberry pi board.
Now we have a new raspberry pi 4 model B 1GB So try to run TensorFlow object detection and then compare with Raspberry pi3B+ also.
The Intel® Distribution of OpenVINO™ toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNN), the toolkit extends computer vision (CV) workloads across Intel® hardware, maximizing performance. The Intel Distribution of OpenVINO toolkit includes the Intel® Deep Learning Deployment Toolkit (Intel® DLDT).
OpenVINO™ toolkit, short for Open Visual Inference and Neural network Optimization toolkit, provides developers with improved neural network performance on a variety of Intel® processors and helps them further unlock cost-effective, real-time vision applications.The toolkit enables deep learning inference and easy heterogeneous execution across multiple Intel® platforms (CPU, Intel® Processor Graphics)—providing implementations across cloud architectures to edge devices. This open source distribution provides flexibility and availability to the developer community to innovate deep learning and AI solutions.
The Intel® Distribution of OpenVINO™ toolkit is also available with additional, proprietary support for Intel® FPGAs, Intel® Movidius™ Neural Compute Stick, Intel® Gaussian Mixture Model - Neural Network Accelerator (Intel® GMM-GNA) and provides optimized traditional computer vision libraries (OpenCV*, OpenVX*), and media encode/decode functions. To learn more and download this free commercial product, visit: https://software.intel.com/en-us/openvino-toolkit
What is Intel Movidius ( Neural Compute Stick )?
The Intel® Movidius™ Neural Compute Stick (NCS) is a tiny fanless deep learning device that you can use to learn AI programming at the edge. NCS is powered by the same low power high performance Intel Movidius Vision Processing Unit (VPU) that can be found in millions of smart security cameras, gesture controlled drones, industrial machine vision equipment, and more.
Face Detection , Object Detection( Object Detection C++ Sample SSD ) Identify faces for a variety of uses, such as observing if passengers are in a vehicle or counting indoor pedestrian traffic. Combine it with a person detector to identify who is coming and going.
To validate OpenCV* installation, you may try to run OpenCV's deep learning module with Inference Engine backend. Here is a Python* sample, which works with Face Detection model.
This model shows the position of the head and provides guidance on what caught the subject's attention.
This demo showcases Object Detection task applied for face recognition using sequence of neural networks. Async API can improve overall frame-rate of the application, because rather than wait for inference to complete, the application can continue operating on the host while accelerator is busy. This demo executes four parallel infer requests for the Age/Gender Recognition, Head Pose Estimation, Emotions Recognition, and Facial Landmarks Detection networks that run simultaneously. You can use a set of the following pre-trained models with the demo:
face-detection-adas-0001, which is a primary detection network for finding faces
age-gender-recognition-retail-0013, which is executed on top of the results of the first model and reports estimated age and gender for each detected face
head-pose-estimation-adas-0001, which is executed on top of the results of the first model and reports estimated head pose in Tait-Bryan angles
emotions-recognition-retail-0003, which is executed on top of the results of the first model and reports an emotion for each detected face
facial-landmarks-35-adas-0002, which is executed on top of the results of the first model and reports normed coordinates of estimated facial landmarks
For more information about the pre-trained models, refer to the https://github.com/opencv/open_model_zoo/blob/master/intel_models/index.md "Open Model Zoo" repository on GitHub*.
This code sample showcases vehicle detection, vehicle attributes, and license plate recognition. The demo uses OpenCV to display the resulting frame with detections rendered as bounding boxes and text.
This demo showcases Vehicle and License Plate Detection network followed by the Vehicle Attributes Recognition and License Plate Recognition networks applied on top of the detection results. You can use a set of the following pre-trained models with the demo:
vehicle-license-plate-detection-barrier-0106, which is a primary detection network to find the vehicles and license plates
vehicle-attributes-recognition-barrier-0039, which is executed on top of the results from the first network and reports general vehicle attributes, for example, vehicle type (car/van/bus/track) and color
license-plate-recognition-barrier-0001, which is executed on top of the results from the first network and reports a string per recognized license plate
For more information about the pre-trained models, refer to the https://github.com/opencv/open_model_zoo/blob/master/intel_models/index.md "Open Model Zoo" repository on GitHub*.
Install OpenVINO on Raspberry Pi
System Requirements
Hardware:
Raspberry Pi* board with ARMv7-A CPU architecture
32GB microSD card
One of Intel® Movidius™ Visual Processing Units (VPU):
