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Detecting vehicles in the video stream is a matter of object detection. Object detection problems can be treated as classification problems or regression problems. In the classification method, the image is divided into small parts, each performed by a classifier to determine if there are objects in the patch. Bounding boxes are assigned to patches with positive classification results. In regression, the entire image directly creates a bounding box of objects in one or more images via a convolution neural network.

The image classification problem is a label that predicts images in predefined labels. It is assumed that there is a single object of interest in the image, which covers an important part of the image. Detection detects not only the object class but also the range of objects in the image. Objects can be placed anywhere in the image and can be of any size (ratio) as shown. In the conventional detection method, a block direction histogram (SIFT or HOG) function that does not validate high precision standard data sets such as PASCAL VOC is used. Since these methods encode the very low level functionality of the object, we can not differentiate between different tags well. A method based on deep learning (convolution network) is a conventional technique for object detection in an image.

In the world of IoT service, machines that are starting to fail may detect problems. The sensor monitors its main functions. When a problem is detected, it is communicated to people (local or remote). You can request repair, the machine will continue to operate longer. Everything exists from the purpose of human life to the purpose of the vacuum cleaner. The purpose is usually derived from the task being executed. If you repeat a task, the role belongs. In society, the succession of the character over time is recognized as a part of the personality of others.

We deal with intruder detection problems analyzing user's behavior on the Internet. Combining data analysis and behavioral psychology is a complex and interesting problem. As an explanation of one of the tasks, Yandex solved the problem of mailbox intruder detection based on user behavior patterns. In other words, the intruder's behavior pattern may be different from the mailbox owner's behavior pattern. To train the first model, you need to prepare the data. First, exclude the target variable from the training set. Both the training set and the test set now have the same number of columns so you can aggregate them into one data frame. Therefore, all transformations are performed on both the training data set and the test data set.