Security and Safety
Computer vision and video system technologies jointly allow to develop the system of protection
AI allows companies to improve security, safety, and access control system effectiveness by using recognition and object detection technologies in real-time.
Warehouse surveillance cameras sometimes need to distinguish between a person in the video or an animal in order to reduce false alarms of security systems. For store video surveillance systems, determine the presence of items that threaten the safety of the store or its visitors.
More details about our experience in annotation for a security and safety system on a construction site can be found here.
We can detect and annotate any objects on the pictures in order to train your data for creating an AI model for visual search applications.
Warehouse surveillance cameras sometimes need to distinguish between a person in the video or an animal in order to reduce false alarms of security systems. For store video surveillance systems, determine the presence of items that threaten the safety of the store or its visitors.
More details about our experience in annotation for a security and safety system on a construction site can be found here.
We can detect and annotate any objects on the pictures in order to train your data for creating an AI model for visual search applications.
Tagias Manager helps you create a good technical task for the data or markup that needs to be performed. So that you get a training dataset that gives real results and a low error rate.
SAFETY
CASES
Use cases
Image annotation
Surveillance systems
Safety
control
control
Face
detection
detection
Training AI to recognize and detect strange objects or activities inside houses, stores
Determination of certain items presence: helmets, gloves, masks,
special equipment
special equipment
In order to provide access control in companies, factories, construction
sites, etc.
sites, etc.
Monitoring equipment and interpreting the data, controlling environmental factors
Predictive maintenance
Bounding boxes
The most common type of data annotation. Bounding boxes are mainly intended to detect objects, delineating their borders with transparent rectangles. Bounding boxes are applied in almost all industries of the data labelling market.
Classification
This method is used to determine the presence of an object in the image. A specific label is assigned to each image. Image tagging helps train AI to recognize objects' classes.
Keypoints
This type of annotation uses dots across an image to identify an object or its details/parts. Keypoints are applied mostly with facial recognition, body parts, and postures.
Polygons
Polygons are used to determine an object's shape and location with maximum precision while avoiding additional noise. Polygon annotation is applied in almost all industries.
Lines
Lines annotation is used for detecting and recognizing lanes. This type of annotation is applied mostly in the automotive industry to make self-driving cars safe.
Semantic segmentation
A data labelling method where objects are delineated in the image at the pixel level, depending on class. It's called semantic segmentation because each pixel has semantic meaning. Segmentation is usually applied in the automotive industry.