Real time object identification using deep convolutional neural networks
Rajeswari Sujana S., Sudar Abisheck S., Tauseef Ahmed A., Sarath Chandran K.R. · 2017
The project on Real Time Object Identification presents an approach to use the concept of deep learning with the convolutional neural networks in identifying the objects present when video is given as the input. The method uses the input video to give the output with the set of identified objects surrounded by boxes, even if the objects are of different sizes and shapes. Along with identification of the objects, the convolutional neural network works give the confidence score for each of the object. This methodology is called the Single Shot MultiBox Detector (SSD). In this project, we are replacing the VGG Net with Residual Networks in the architecture to increase the computational speed.