Computer Vision

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Computer Vision in Video surveillance

Our software makes video surveillance smart, changing cameras from just seeing to thinking – object detections, face recognitions, car plate recognitions and real-time video content analysis.


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Video surveillance software
Web Camera Pro is using Computer Vision to understand everything that happens on your cameras and sends smart and flexible video surveillance alerts.

Make more intelligent business decisions, achieve greater efficiency and effectiveness, and implement powerful security for people, facilities and assets - all with less cost, and less effort. Our award-winning products and services let you do all this, and more.

Today's AI systems can go a step further and take actions based on an understanding of the image.

There are many types of computer vision that are used in different ways:

· Image segmentation partitions an image into multiple regions or pieces to be examined separately.
· Object detection identifies a specific object in an image.
· Facial recognition is an advanced type of object detection that not only recognizes a human face in an image, but identifies a specific individual.
· Edge detection is a technique used to identify the outside edge of an object or landscape to better identify what is in the image.
· Pattern detection is a process of recognizing repeated shapes, colors and other visual indicators in images.
· Image classification groups images into different categories.
· Feature matching is a type of pattern detection that matches similarities in images to help classify them.


Web Camera Pro is video surveillance software
Video Tutorial:
1) How to use video surveillance software?
2) How to add new video surveillance devices?
3) How to view video surveillance archive?
4) How to send events to Telegram?
5) How to stream IP Camera to YouTube?
Video Surveillance Software
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Computer vision in video surveillance
Computer vision, or the ability of artificially intelligent systems to "see" like humans, has been a subject of increasing interest and rigorous research now. As a way of emulating the human visual system, the research in the field of computer vision purports to develop machines that can automate tasks that require visual cognition. However, the process of deciphering images, due to the significantly greater amount of multi-dimensional data that needs analysis, is much more complex than understanding other forms of binary information. This makes developing AI systems that can recognize visual data more complicated.
But, the use artificial neural networks is making computer vision more capable of replicating human vision. Computer vision technology of today is powered by deep learning algorithms that use a special kind of neural networks, called convolutional neural network (CNN), to make sense of images. These neural networks are trained using thousands of sample images which helps the algorithm understand and break down everything that's contained in an image. These neural networks scan images pixel by pixel, to identify patterns and "memorize" them. It also memorizes the ideal output that it should provide for each input image (in case of supervised learning) or classifies components of images by scanning characteristics such as contours and colors. This memory is then used by the systems as the reference while scanning more images. And with every iteration, the AI system becomes better at providing the right output.

Web Camera Pro application uses data vision theory to identify objects within video, search through catalogues of images, and extract information out of images. Computer vision provides the functions for recognizing and identifying an image as a specific object.

Object detection deals with detecting instances of semantic objects of a certain class (such as a dog, a human, or a car.) in digital images and videos.

Video tracking is the process of locating a moving object (or multiple objects) over time. Video tracking is an active research topic in the computer vision community and is a prerequisite for many tasks, such as human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging, and video editing. The challenge is to track the object irrespective of scale, rotation, perspective projection, occlusions, changes of appearance, and illumination. However, it should be fast enough to maintain transparent interaction with the user.

The digital images taken must be pre-processed to improve their quality before they are analyzed. Using digital filtering, the noise in the image can be removed and the contrast enhanced. Sometimes in this step the color image is converted to a gray-scale image, called the intensity image. The intensity is used to divide the images into disjointed regions with the purpose of separating the region of interest from the background.

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