Training high dimension ternary features with GA in boosting cascade detector for object detection
Qian Chen, Kazuyuki Masada, Haiyuan Wu, Toshikazu Wada · 2008
Viola et al. have introduced a fast object detection scheme based on a boosted cascade of haar-like features. In this paper, we introduce a novel ternary feature that enriches the diversity and the flexibility significantly over haar-like features. We also introduce a new genetic algorithm (GA) based method for training effective ternary features through iterations of feature generation and selection. Experimental results showed that the rejection rate can reach at 98.5% with only 16 features at the first layer of the constructed cascade detector. This indicates the high performance of our method for generating effective features. We confirmed that the training time can be significantly shortened compared with Violas's method while the performance of the resulted cascade detector is comparable to the previous methods.