Traffic congestion in developing cities like Nairobi, Kenya can be significantly impacted by the presence of Moving Traffic Obstacles (MTOs). These MTOs are those events that temporarily exist on the road, moving with or against the direction of traffic at slower speeds. They include two-wheelers, pushcarts, animals, and pedestrians, which have quite different influence on traffic compared with static obstacles, such as potholes and speed bumps. As Smartphones and supporting 3G infrastructures are wide spread even in developing countries, recent studies enabled frugal traffic obstacle data collection from smartphones in probe cars. Assuming the sparse and errorful observation of traffic obstacles, we propose an MTO detection algorithm extending an image analysis technique called Probabilistic Hough Transform for probabilistic observations as input. Based on our experiences with a small set of real-world data collected in a smartphone-based probe car project with Nairobi City County, we conducted experiments with simulated observation data to see the effectiveness of the algorithm.