Internet of Things is an emerging field wherein a lot of classical approaches can be inculcated. One such approach is found in image processing domain. It is real-time object detection and recognition. Object recognition is considered as a complicated process because the object can be of any shape, size or color. Object detection can be performed with effectiveness by using various prevalent techniques such as Scale Invariant Feature Transform (SIFT), a faster version known as Speeded-Up Robust Features (SURF) and the combination of two very efficient algorithms called as Oriented FAST and Rotated BRIEF (ORB) and so on. Although different techniques are dedicated to the different type of objects. In this paper, an effort has been made to combine the object recognition technique with Internet of Things (IoT) concept. The IoT device acting as an input is the camera that captures the image. The object present in the image is detected and recognized. After that, its information is extracted through the internet and displayed on the screen along with the recognized object. The recognition takes place using the pre-existing database. The database consists of the objects that have salient features which would make the task of recognition unambiguous. The bag of features method is considered in order to make recognition effective. The effective use of Internet of Things is carried out by establishing communication between a camera which acts as an input device and visual output devices. This communication takes place over Internet protocol. In the case of object detection, various parameters such as rotation invariance, scale invariance, intensity change, orientation invariance and partial object detection are also considered to make the system robust. Time consideration is carried out to make the system work in real time.