Classification of Objects in Video Records using Neural Network Framework
Abhiraj Biswas, Arka Prava Jana, Mohana, S Sai Tejas · 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2018
Object Classification is a principle task in image and video processing. It is exercised over a multitude of applications ranging from test and number classification to traffic surveillance. The primitive machine learning concepts had provided the pedestal for carrying out umber of image processing tasks. Classifier such as Haar cascade which uses Haar like features was primitively used for face detection. Nowadays it's used for tacking and detection purposes also. Moreover, due to the ever-increasing demand and scope of improvement in the existing fields, the primitive methods need a lot of upgradation. Neural Networks have made the tasks quite plain sailing. Right from the vanilla neural networks to Fast R-CNN and then Faster R-CNN, all models have contributed significantly in the domain of computer vision. This paper mainly focuses in detection and classification ranging from single class objects to multi class objects. The Haar cascade classifier was trained on a batch of positive and negative samples which were later stitched together to form a vector file and finally form the xml file. On the other hand, COCO dataset used for implementing R-CNN algorithm due to the presence of pertained model in it.