A method for performing three-dimensional localization, preferably of traffic participants including vehicles or pedestrians, requiring only a single camera, comprising: capturing images from the single camera; generating a cue combination from dense stereo (s), sparse features (p), and 2D object bounding boxes (d), using observation data z of each cue, comprising: for each cue i from {s, p, d} and frame k, fitting a model A k i whose parameters reflect the expected accuracy of the respective cue from the observation data z; determining observation variances v k i from the parameters of the model A k i using a model C i for each cue i obtained from training data; and determining observation covariances U k i from the observation variances v k i. wherein if i = s: U k s = diag (ν k s,n1, ν k s,n3, ν k s,h), wherein n1, n3 are elements of a ground plane n = (n1, n2, n3) and h is the ground plane height; ifi = p: U k p = ν k p,h; if i = d: U k,n d = ν k,n d, for each obj ect n; calculating the cue combination U k by combining the observation covariances U k i from each cue i from {s, p, d} according to; U k = ∑ i = 1 m U i k − 1 − 1; calculating ground plane observation data z k using the cue combination U k and the observations z k i from each cue i from {s, p, d} according to: z k = U k ∑ i = 1 m U i k − 1 z i k. estimating a ground plane ( n, h) with a Kalman filter using the cue combination U k and the ground plane observation data z k in the step of the Kalman filter in which the observation z is updated; correcting for scale in monocular structure from motion, using the height h of the estimated ground plane ( n, h); and performing localization by combining SFM, ground plane and 2D object bounding boxes to produce a 3D object bounding box localization. capturing images from the single camera; generating a cue combination from dense stereo (s), sparse features (p), and 2D object bounding boxes (d), using observation data z of each cue, comprising: for each cue i from {s, p, d} and frame k, fitting a model A k i whose parameters reflect the expected accuracy of the respective cue from the observation data z; determining observation variances v k i from the parameters of the model A k i using a model C i for each cue i obtained from training data; and determining observation covariances U k i from the observation variances v k i. wherein if i = s: U k s = diag (ν k s,n1, ν k s,n3, ν k s,h), wherein n1, n3 are elements of a ground plane n = (n1, n2, n3) and h is the ground plane height; ifi = p: U k p = ν k p,h; if i = d: U k,n d = ν k,n d, for each obj ect n; fitting a model A k i whose parameters reflect the expected accuracy of the respective cue from the observation data z; determining observation variances v k i from the parameters of the model A k i using a model C i for each cue i obtained from training data; and determining observation covariances U k i from the observation variances v k i. wherein if i = s: U k s = diag (ν k s,n1, ν k s,n3, ν k s,h), wherein n1, n3 are elements of a ground plane n = (n1, n2, n3) and h is the ground plane height; ifi = p: U k p = ν k p,h; if i = d: U k,n d = ν k,n d, for each obj ect n; if i = s: U k s = diag (ν k s,n1, ν k s,n3, ν k s,h), wherein n1, n3 are elements of a ground plane n = (n1, n2, n3) and h is the ground plane height; ifi = p: U k p = ν k p,h; if i = d: U k,n d = ν k,n d, for each obj ect n; calculating the cue combination U k by combining the observation covariances U k i from each cue i from {s, p, d} according to; U k = ∑ i = 1 m U i k − 1 − 1; calculating ground plane observation data z k using the cue combination U k and the observations z k i from each cue i from {s, p, d} according to: z k = U k ∑ i = 1 m U i k − 1 z i k. estimating a ground plane ( n, h) with a Kalman filter using the cue combination U k and the ground plane observation data z k in the step of the Kalman filter in which the observation z is updated; correcting for scale in monocular structure from motion, using the height h of the estimated ground plane ( n, h); and performing localization by combining SFM, ground plane and 2D object bounding boxes to produce a 3D object bounding box localization.