Real Time Object Recognition Using OpenCV and Numpy in Python

Object recognition is the process of recognizing objects based on their characteristics like color, shape, and with the particular occurrence of the object in digital videos and as well as normal daily life. This object detection is the already known and introduced technology that performs the program using computer technology as the platform and using some image processing units for the detection of the objects using some class of data sets given by the user. Dual priorities, an imbalance in the classes, a lack of data, etc. are a few significant challenges. As this object detection is working as a real-time application and is mostly used for tracking objects, counting crowds, and self-driving cars, and even more useful in traffic control and also for security surveillance purposes which is the major purpose of usage. Some techniques like deep learning-based object detection a relatively new approach that has blurred the lines between object localization and detection. Region-Based Convolutional Neural Network was used to deal with it. Another was a fast Region-Based Convolutional Neural Network, which was proposed in Python and C++ and raises the problem of Region-Based Convolutional Neural Network and Spatial Pyramid Pooling-net when dealing with speed and accuracy. It was resolved using a deep VGG16 network. This application eventually came up with some basic concepts that are likely OpenCV, an open-source library with some functions that can be used for object detection, and the combination of python 2.7, improving the accuracy and efficiency of object detection. Even though many applications are available initially, by using the simple functions in OpenCV and NumPy, the proposed work achieves accurate results in finding the objects easily.