چكيده به لاتين
Today, when it comes to image processing, detection and identification of real-time moving objects with high accuracy due to excessive data volume, is a very challenging task. This new subject has a great impact on the visual surveillance of and because of the continual change of the target item and the changing properties the thing, view, obstruction, appearance and movement and brightness, this topic is one of the important research areas in security monitoring systems and surveillance systems. It is visual. In this context, most traditional methods of Detection of objects is based on on manual features and old architectures and have their drawbacks. At present, despite the various methods when it comes to deep learning, to address the challenges of traditional methods, we can introduce techniques that are able to learn semantic features, high level and deeper. In this regard, in this research, a new system in order to detect and classification of moving objects is presented. This new approach involves the extraction of new features by the deep convolutional learning algorithm (CNN), the selection of features by the grasshopper optimization algorithm, and finally the classification of these features, which effectively simplifies the situation of the recommended approach. On the whole, the CNN method has many advantages, such as short training time, good generalization performance, and fast computational speed, compared to other sophisticated techniques. In this method, by combining optimization algorithms and CNN, better accuracy is created compared to conventional CNN.
