1. A method of predicting a target type in a set of target types from multiple images, comprising: obtaining first and second images; compiling a first set of possible detections corresponding to regions of interest within the first image and a second set of possible detections corresponding to regions of interest within the second image; associating each of the regions of interest from the first set of possible detections with a corresponding one of the regions of interest from the second set of possible detections; determining a first set of confidence values and associated azimuth angles for each target type in the set of target types from the first image; determining a second set of confidence values and associated azimuth angles for each target type in the set of target types from the second image; fusing the first set of confidence values and the second set of confidence values in accordance with the associated regions of interest for each of the azimuth angles to produce a fused curve for each target type in the set of target types, each point in the fused curve being derived from one of the first set of confidence values and a corresponding one of the second set of confidence values for each azimuth angle; and predicting the target type from the set of target types based on at least one of a maximum value of the fused curve, a maximum area under the fused curve and a maximum area under the fused curve within an azimuth angle window.