CANSS: A Candidate Selection and Search Algorithm to Initialize Car Tracking
Frank Dellaert · 1997
This report describes an algorithm to find cars in images taken by a forward-looking camera, mounted on a pursuing vehicle. It was developed in order to initialize a car tracker described elsewhere [2]. The algorithm proceeds in two steps, in order to find a bounding box that most probably corresponds to a car to be tracked. In a candidate selection step, potential edges for each of the 4 sides of the bounding box are selected. This is done using non-parametric density estimation followed by detection of local maxima. Then, in the search step, the most probable bounding box is selected from a set of hypotheses obtained by combining the candidate edges in all possible ways. The resulting algorithm is simple, fast, and works well in practice.