Collision risk estimation from an uncalibrated moving camera based on feature points tracking and clustering

We present an approach to estimate collision risk using a single uncalibrated camera attached to a moving platform. The proposed approach is based on computing the local scale change from image motion information obtained by tracking feature points. A fuzzy logic based thresholding step is applied to the tracked feature points to obtain the set of feature points that most likely represent the potential obstacle. The resultant set of points is clustered and the time-to-collision values for the corresponding clusters can be computed to determine the risk of collision. We perform collision detection experiments on three image sequences obtained from a moving car. The results indicate that the proposed approach can estimate collision risk with a single uncalibrated camera.