Telecom Inventory Management via Object Recognition and Localisation on Google Street View Images

We present a novel method to update assets for telecommunication infrastructure using google street view (GSV) images. The problem is formulated as a object recognition task, followed by use of triangulation to estimate the object coordinates from sensor plane coordinates, To this end, we have explored different state-of-the-art object recognition techniques both from feature engineering and using deep learning namely HOG descriptors with SVM, Deformable parts model (DPM), and Deep learning (DL) using faster RCNNs. While HOG+SVM has proved to be robust human detector, DPM which is based on probabilistic graphical models and DL which is a non-linear classifier have proved their versatility in different types of object recognition problems. Asset recognition from the street view images however pose unique challenge as they could be installed on the ground in various poses, orientations and with occlusions, objects camouflaged in the background and in some cases inter class variation is small. We present comparative performance of these techniques for specific use-case involving telecom equipment for highest precision and recall. The blocks of proposed pipeline are detailed and compared to traditional inventory management methods.