Neurotechnology, a provider of high-precision biometric identification and object recognition technologies, has released SentiVeillance 6.0 software development kit (SDK), which provides improved facial recognition using up to 10 surveillance, security and public safety cameras on a single computer.
The new version uses deep neural-network-based facial detection and recognition algorithms to improve accuracy, and it utilizes a Graphing Processing Unit (GPU) for enhanced speed.
(Automated surveillance systems can use SentiVeillance (known previously as VeriLook Surveillance) for biometric face recognition and pedestrian/vehicle tracking in live video streams. Courtesy of Neurotechnology and YouTube)
Users can now also more quickly and easily adjust the tradeoff between speed and accuracy as needed for different applications.
“We developed SentiVeillance 6.0 as a self-adapting system based on deep neural networks that were trained on a larger quantity of data,” said Ignas Namajunas, surveillance technologies research lead for Neurotechnology.
“This ensures better generalization for a variety of conditions.”
“Additionally, by making use of the GPU processing capabilities, we were able to improve the processing speed significantly.”
Because SentiVeillance 6.0 can process information from up to 10 surveillance cameras with one GPU, it provides faster, easier, more accurate identification of faces against watch lists, making it suitable for a wide range of surveillance applications.
As with previous versions, the new SentiVeillance also provides real-time moving object detection; tracking and classification for pedestrians, vehicles and other predefined object classes based on size and speed of movement; and area control that triggers “events” when people or objects enter, leave or stay in restricted areas.
The SentiVeillance 6.0 technology has these specific capabilities:
Real time performance.
SentiVeillance technology performs face, pedestrian or object recognition and tracking in real time. The technology is designed to run on multi-core processors to achieve fast performance.
Two algorithms for surveillance systems
Depending on the surveillance system design, one of these algorithms may be used:
Biometric face recognition algorithm is based on deep neural networks and provides these capabilities for surveillance systems:
Multiple face detection, features extraction and template matching with the internal database in real time.
Facial identification reliability enables using large watchlist databases.
Face tracking is performed in all successive frames from the video source until they disappear from camera field of view. The face tracking algorithm uses dynamic face and motion prediction models that make it robust to occlusions like other objects or even other faces. The algorithm is able to continue tracking a face even when it re-appears after being fully covered by occlusions (like walls, furniture, posters etc).
Gender classification (optional) for each person in the frame. See video below.
Age determination (optional) for each person in the frame.
Smile, open-mouth, closed-eyes, glasses, dark-glasses, beard and mustache attributes detection (configurable). See video below.
Motion detection and tracking algorithm performs advanced detection of moving objects in the scene, their classification and tracking until they disappear,
These features are available for surveillance systems:
After calibration SentiVeillance allows to perform object classification based on the size and movement speed.
For example, users can configure a surveillance system to determine if a tracked object is a vehicle, a single pedestrian or group of pedestrians. See video below.
Restricted areas control
SentiVeillance algorithm can detect and report if people or objects enter, leave or stay in restricted areas.
The events are triggered when people or objects cross pre-defined lines or enter polygon-shaped areas. See video below.
Tolerance to weather conditions.
The algorithm ignores rain and snow, as well as trees and bushes, which are swayed by wind.
A system based on SentiVeillance 6.0 SDK is able to log face, pedestrian or object appearance, disappearance and tracking.
The detected faces are matched against the watchlist in the internal database and recognized faces are immediately reported to the system.
The system uses face tracking for automatic enrollment from video stream and adding new facial templates to watch list on the fly.
Large surveillance systems support
SentiVeillance 6.0 SDK allows to integrate its technology into surveillance systems with multiple cameras and multiple data-processing nodes.
A single computer can process video data from up to 10 cameras simultaneously. Multiple computers with running SentiVeillance software can quickly synchronize biometric and surveillance data between each other over the network.
The synchronization can be customized as the SDK includes sample source code for using the communication and synchronization processes.
Video files processing
SentiVeillance also accepts data from video files.
The video files are processed in real time as coming from a virtual camera, therefore an hour-long video will be processed in one hour.
Identification against watchlist
(SentiVeillance SDK: Face Tracking And Identification. Courtesy of Neurotechnology and YouTube)
Gender classification & facial attributes
(SentiVeillance SDK: Face Atributes Detection Gender Glasses. Courtesy of Neurotechnology and YouTube)
Vehicle and pedestrians tracking
(SentiVeillance SDK: Vehicle and Pedestrian Tracking. Courtesy of Neurotechnology and YouTube)
Pedestrians and restricted zone tracking
(SentiVeillance SDK: Pedestrian Tracking and Restricted Zone Triggering. Courtesy of Neurotechnology and YouTube)
The SentiVeillance 6.0 SDK is available through Neurotechnology or from distributors worldwide.
As with all Neurotechnology products, the latest version is available as a free upgrade to existing SentiVeillance customers and a trial version is also available at www.neurotechnology.com.