Pan Tilt Traffic Cameras (PTTC) are a vital component of traffic managementsystems for monitoring/surveillance. In a real world scenario, if a vehicle is in pursuit of another vehicle or an accident has occurred at an intersection causing traffic stoppages, accurate and venerable data from PTTC is necessary to quickly localize the cars on a map for adept emergency response as more and more traffic systems are getting automated using machine learning concepts. However, the position(orientation) of the PTTC with respect to the environment is often unknown as most of them lack Inertial Measurement Units or Encoders. Current State Of the Art systems 1. Demand high performance compute and use carbon footprint heavy Deep Neural Networks(DNN), 2. Are only applicable to scenarios with appropriate lane markings or only roundabouts, 3. Demand complex mathematical computations to determine focal length and optical center first before determining the pose. A compute light approach "TIPANGLE" is presented in this work. The approach uses the concept of Siamese Neural Networks(SNN) encompassing simple mathematical functions i.e., Euclidian Distance and Contrastive Loss to achieve the objective. The effectiveness of the approach is reckoned with a thorough comparison study with alternative approaches and also by executing the approach on an embedded system i.e., Raspberry Pi 3.