There has been further research built on the cascaded VJ approach to car detection. Han et al incorporated vertical edges, the car’s shadow and intensity during hypothesis generation and feature symmetry during verification for more accurate detection [19].
[19] Han, Sungji, Youngjoon Han, and Hernsoo Hahn. "Vehicle detection method using Haar-like feature on real time system." World Academy of Science, Engineering and Technology 59 (2009): 455-459.
Its not clear that this will always be effective. Some FP hypotheses will be symmetrical.
Also, some TP will be non-symmetrical if the training hasn't produced a symmetrical model (even though ideally it should be close to symmetric for frontbacks). But even in side cars, there is not complete shape symmetry.
Its not clear that this will always be effective. Some FP hypotheses will be symmetrical. Also, some TP will be non-symmetrical if the training hasn't produced a symmetrical model (even though ideally it should be close to symmetric for frontbacks). But even in side cars, there is not complete shape symmetry.
This approach could be tried.