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| Detector that uses neural network | Action of the neural network | |||||
|---|---|---|---|---|---|---|
| Face detector TV | Detection of faces and masks | |||||
| Face detector VIFace detector VA | ||||||
| Face detector VL | ||||||
| Masks Mask detector VIMasks detector VL | ||||||
| Masks Mask detector VA | ||||||
| Masks Mask detector TV | ||||||
| License Plate Recognition plate recognition BRS | Detection of vehicle license plates | |||||
| License Plate Recognition plate recognition IV | ||||||
| License Plate Recognition plate recognition RR | ||||||
| License Plate Recognition RR—Search In Archiveplate recognition RR—Search in archive | ||||||
| License Plate Recognition plate recognition RR—Parking | ||||||
| License Plate Recognition plate recognition VT | ||||||
| Vehicle Recognition recognition XR | ||||||
| Vehicle Recognition Vehicle recognition RR | Detection of vehicle attributes | |||||
| Object Trackertracker with a neural network filter | Processes tracker results and filters out objects of no interest in a complex video image (foliage, glare, and so on) | |||||
| Object Tracker Human tracker VL | Detection of people in the frame | |||||
| Fight detector VL | Detection of fights in the frame | |||||
| Stopped object detector | Detection of stopped objects in the frame | |||||
| Abandoned Objects Detection object detector VI | Detection of abandoned objects or objects that disappeared in the frame | |||||
| Abandoned Objects Detection object detector VI (Street) VI | ||||||
| Line crossing VI | Detection of object movement in the prohibited direction | |||||
| Movement in prohibited direction VI | ||||||
| Motion in area detector VI | Detection of object movement in a given area | |||||
| Detector of atypical changes in the scene VI | Detection of changes not typical for the scene, such as overexposure, darkening, or defocusing | |||||
| NeurotrackerNeural tracker | Detection of the position of only the required objects in the frame | |||||
| NeurocounterNeural counter | Counts the number of objects | |||||
| Fire Detectiondetector | Detection of fire | |||||
| Smoke Detectiondetector | Detection of smokeObject Presence Detection | |||||
| Neural classifier | Detection of a specific object | |||||
Pose Detection Human pose detector and its sub-detectors:
| Identifies each person's "skeleton" and detects poses that can represent a security threat | |||||
| Equipment detection (PPE)detector | Detection of necessary equipment and personal protective equipment. Segmenting and classification neural networks are used for the operation of the Equipment detection (PPE)detector (see Functions of the Equipment detection (PPEdetector)) | |||||
| PPE detection Equipment detector VL | ||||||
| Person-based privacy masking | Segmenting neural network breaks down the human body into areas | |||||
| Privacy masking | Detection and masking of moving and stationary objects | |||||
| Water Level Detectionlevel detector | Increases the accuracy of water level detection in difficult conditions. For example, in scenes with clear water | |||||
| Audio Analytics Detection classification IV | Detection of different sounds: from a baby cry to the sound of a gunshot | |||||
| Crowd Estimation estimation VA | Counts crowds of people in a specified area | |||||
| Barcode Detectiondetector | Detection of a specific type of barcode | |||||
| Meta-detector | First neural network analyzes Analyzes text queries and generates a feature vector. Second neural network analyzes the image and also generates a feature vector. Based on the comparison and accumulation of the results of the two vectors, an event is generated if enough negative results are accumulatedimages to identify a match between them |
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