Neurotechnology SentiVeillance 8.0 SDK: Creating identification by using live video streams

Neurotechnology announced the release of the SentiVeillance 8.0 software development kit (SDK). With SentiVeillance SDK, developers can create identification solutions that use live video streams from digital surveillance cameras or video files.

The latest version adds face detection and recognition of people who are wearing masks and includes new algorithms that improve license plate detection and recognition speed and accuracy.

It also provides new features for vehicle and human (VH) mode, including car make and model estimation, vehicle angle estimation and cloth and gender estimations for pedestrians. It includes a new working mode combination enabling face and VH modes to be used together for fast and accurate identification.

“I am very grateful for all the hard work our team put into this latest release that enhances speed and accuracy and provides compelling new ways to use our software,” said Vytautas Pranckenas, SentiVeillance product lead for Neurotechnology.

“There has never been a more important time for reliable recognition solutions that can adapt to new conditions, such as people wearing masks.”

The new SentiVeillance mode combination (face and VH) allows tracking of the subject even when the face is no longer visible – functionality that is particularly useful in scenarios where tracking a person’s position is important.

Biometric facial recognition

The high reliability of SentiVeillance’s biometric facial identification algorithm allows it to be used with large watch-list databases, both for identifying a person who is on a list and adding new faces from video streams to watch-lists.

It tracks identified faces as they move around the camera’s field of view and continues tracking even if a person moves behind an object and re-appears. It is effective both close-in and at a distance when using high-resolution cameras.

SentiVeillance can perform gender classification, evaluate a person’s age, identify facial expressions (e.g. smile, open mouth, closed eyes) and detect particular attributes, such as if the person is wearing a mask, glasses or sunglasses and whether they have a beard or mustache.

Vehicle and human detection and movement tracking (VH)

SentiVeillance detects both moving and static vehicles or people in a scene and performs object classification and tracking until the subjects disappear. In addition to pedestrian detection and vehicle classification by type, it provides identification of the specific vehicle make and model as well as its orientation.

For pedestrians, SentiVeillance identifies different types of clothing and provides gender prediction. The algorithm also returns an estimation of paint color for vehicles and predominant clothing color for pedestrians, and it determines the vector in which they are moving (e.g. north, south, southwest).

Automatic license plate recognition (ALPR)

The new ALPR capability in SentiVeillance automatically detects and reads vehicle license plates, recording the information from both stationary and moving vehicles within the scene.

The latest release is up to 2 times faster for CPU uses and up to 5 times faster when a GPU is used. Accuracy tests reach 99.1% for greater than 200 pixel width license plates and 88.7% for license plates captured from far away (less than 100 pixel width).

The latter two modes (VH and ALPR) can be used together to create larger, more varied solutions. For example, when conventional ALPR is used for road tolls, automatic car washes or paid parking systems, users might try to avoid paying by altering or exchanging license plates.

Stolen vehicles might also have their license plates changed. When using multiple analytics in concert, the resulting solution could match and verify plate numbers with other characteristics of the vehicle, such as type and color, through queries of previously stored values or vehicle registration databases.

The new SentiVeillance is designed to run on multi-core processors for fast performance and can process video data from multiple cameras simultaneously using a common PC (current generation i7 CPU with 8 or more cores) and can utilize multiple graphics processing units (GPUs) to achieve even better performance.

It can be used with large surveillance systems, incorporating many cameras and data-processing nodes. Developers have many and varied options in the creation of scalable, cost-effective solutions for their customers.

Thermal faces sample

The new SentiVeillance SDK package contains a precompiled sample with source codes and device integration for the Mobotix m16 camera with thermal sensor.

It shows functionality for face tracking, mask estimation and temperature readings, and it allows the setting of temperature thresholds or estimating deviations from averages. The sample is created to help with automatic entrance control.

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