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Modern cities have hundreds to thousands of traffic cameras distributed across them, many of them with the capa- bility to pan and tilt, but very often these pan and tilt cameras either do not have angle sensors or do not provide camera orientation feedback. This makes it difficult to robustly track traffic using these cameras. Several methods to automatically detect the camera pose have been proposed in literature, with the most popular and robust being deep learning-based approaches. However, they are compute intensive, require large amounts of training data, and generally cannot be run on embedded devices. In this paper, we propose TIPAngle – a Siamese neural network, lightweight training, and a highly optimized inference mechanism and toolset to estimate camera pose and thereby improve traffic tracking even when operators change the pose of the traffic cameras. TIPAngle is 18.45x times faster and 3x more accurate in determining the angle of a camera frame than a ResNet-18 based approach. We deploy TIPAngle to a Raspberry Pi CPU and show that processing an image takes an average of .057s, equating to a frequency of about 17Hz on an embedded device.