An Efficient Shared Multi-Class Detection Cascade

We propose a novel multi-class object detector, that optimizes the detection costs while retaining a desired detection rate. The detector uses a cascade that unites the handling of similar object classes while separating off classes at appropriate levels of the cascade. No prior knowledge about the relationship between classes is needed as the classifier structure is automatically determined during the training phase. The detection nodes in the cascade use Haar wavelet features and Gentle AdaBoost, however the approach is not dependent on the specific features used and can easily be extended to other cases. Experiments are presented for several numbers of object classes and the approach is compared to other classifying schemes. The results demonstrate a large efficiency gain that is particularly prominent for a greater number of classes. Also the complexity of the training scales well with the number of classes.