Applications and Tools

Explore the capabilities of Metavision SDK by following one of these paths:


Metavision SDK can be used for a variety of application fields:




Particle Size Monitoring

Control, count and measure the size of objects moving at very high speed

in a channel or a conveyor. Get instantaneous quality statistics

in your production line, to control your process.

metavision_psm in C++

metavision_psm in Python

Object Tracking

Track moving objects in the field of view. Leverage the low data-rate

and sparse information provided by event-based sensors to track

objects with low compute power.

metavision_generic_tracking in C++

metavision_generic_tracking in Python

Vibration Monitoring

Monitor vibration frequencies continuously, remotely, with pixel precision

by tracking the temporal evolution of every pixel in a scene.

metavision_vibration_estimation in C++

metavision_vibration_estimation in Python

Spatter Tracking

Track small particles with spatter-like motion

metavision_spatter_tracking in C++

metavision_spatter_tracking in Python

High-Speed Counting

Count objects at unprecedented speeds, high accuracy, generating less data

and without any motion blur.

metavision_counting in C++

metavision_counting in Python

Edgelet Tracking

Track 3D edges and/or Fiducial markers for your AR/VR application

metavision_model_3d_tracking in C++

metavision_model_3d_tracking in Python

Optical Flow

Understand motion through continuous pixel-by-pixel tracking

and not sequential frame by frame analysis.

metavision_sparse_optical_flow in C++

metavision_dense_optical_flow in C++

metavision_sparse_optical_flow in Python

metavision_dense_optical_flow in Python

Active Marker 2D Tracking

Track Active Markers in 2D.

metavision_active_marker_2d_tracking in C++

Active Marker 3D Tracking

Track Active Markers in 3D.

metavision_active_marker_3d_tracking in C++

ArUco Marker Tracking

Track ArUco Markers

metavision_aruco_marker_tracking in C++


For optimal results when using samples listed above, you should pay attention on event stream quality and/or background noise. To deal with that, hardware settings/filters (biases and hardware ESP) and software filters (demonstrated in samples Noise Filtering and Filtering) are available.

In the case of ESP note that AFK and STC functions are available both in hardware and software versions.

Metavision SDK can also manipulate event-based datasets and design event-based Neural Networks. With our pre-trained models, inference can be done with the following use cases:




Detection Inference

Leverage our pretrained automotive model written in pytorch,

and experiment live detection & tracking

metavision_detection_and_tracking_pipeline in C++

metavision_detection_and_tracking_pipeline in Python

Optical Flow Inference

Predict optical flow from Event-Based data leveraging our pretrained

Flow Model, customized data loader and collections of loss function

and visualization tools

flow_inference in Python

Corner Detection & Tracking Inference

Detect and track corners in an event stream with high efficiency.

This method can generate stable keypoints and very long tracks

demo_corner_detection in Python

Gesture Classification Inference

Run a live Rock Paper Scissors game with our pre-trained model

on live stream or on event-based recordings.

classification_inference in Python


Currently there is no sample dedicated to speed measurement. However, if you want to estimate the velocity of objects or an event stream for your application, you can have a look at the sample metavision_sparse_optical_flow (C++ version and Python version) or the sample metavision_spatter_tracking (C++ version and Python version).


Metavision SDK comes with a set of ready-to-use tools:



Metavision Studio

Advanced Graphical User Interface to visualize and record data streamed

by Prophesee-compatible event-based vision systems

Metavision Viewer

Basic Graphical User Interface to visualize and record data streamed

by Prophesee-compatible event-based vision systems

Metavision XYT

Displays events in a 3D space

Metavision Data Rate

Displays event rate from an event-based camera or from a recorded file

Camera Focusing

Focuses an event-based camera using a blinking pattern

Camera Intrinsic Calibration

Estimates intrinsics parameters of EB camera using a blinking pattern

Show Calibrated Poses

Visualizes the locations where the calibration pattern was detected

Ground Plane Calibration

Computes the 4x4 “world to Camera” transformation matrix

Metavision Platform Info

Prints information on the OS, connected devices and installed software

Metavision Software Info

Prints information on the installed software (version, date etc.)

Metavision File Info

Prints information about a RAW, DAT or HDF5 event file

File to CSV Converter

Converts a RAW, DAT or HDF5 event file to a CSV file

File to HDF5 Converter

Converts a RAW or DAT file to an HDF5 event file

File to video Converter

Converts a RAW, DAT or HDF5 event file to an AVI video

File to DAT Converter

Converts a RAW or HDF5 event file to a DAT-formatted file

Active Pixel Detection

Detects and masks active pixels for GenX320 sensor


Metavision Player is a legacy application to read and record event-based data. It is not supported with our last generation of sensors but can still be useful to some as it provides an Analysis Mode to control data visualization, replay, and export that is not yet ported to our other tools.


Except for Metavision Studio, the source code of all those tools is available so that you can tune their behaviour according to your needs or use them as code examples to start programming. Hence those tools are also listed in our Code Samples section. If you want to get the source code of Metavision Studio, check our packaging offers and consider acquiring Metavision SDK Pro.