car object detection

Object Detection - оne of the fastest free software for detecting objects in real time and car numbers recognition. After input of an image, noise is eliminated by smoothing, and the size of the image is normalized by using a pyramid data structure. The features of the image are obtained by means of filtering or Fourier transformation. A pattern that matches a standard form is chosen. The most suitable pattern is selected by using dynamic programming and the distance between the input image and the standard image.
Have you heard about special requirements and the need for a powerful server for video analytics? Have you been told tales about the fact that real-time detection requires special powerful servers?

Our product refutes this claim!

Object Detection video surveillance software turns your computer into a powerful video-security system, allowing you to watch what's going on in your home or business remotely.

The program allows automatic recognition of car numbers (license plates).

Software is based on modern technologies based on neural networks, trained on large data sets.

As specific event occurs Object Detection software will automatically upload video to the server. You'll be able to review all webcasts and events using your YouTube channel.

Our app also saves disc space and equipment costs. Time-lapse mode is available 24/7 for storage.
The current version supports Windows 7 - 10 (64-bit). In the future, the program will be released for other platforms.

Object Detection 5.0 allows the detection of objects in real-time on standard computer. Of course, if your computer has a GPU graphics card and supports CUDA, then the performance will be even higher.

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.