Enhanced Codebook Model and Fusion for Object Detection with Multispectral Images

The Codebook model is one of the popular real-time models for object detection. In our previous work, we have extended it to multispectral images. In this paper, two methods to impove the previous work are proposed. On one hand, multispectral self-adaptive parameters and new estimation criteria are exploited to enhance codebook model. On the other hand, the approach of fusion is explored to improve the performance on multispectral images by fusing the detection results of the monochromatic bands. For the enhancements of codebook model, the self-adaptive parameter estimation mechanism is developed based on the statistical information of the data themselves, with which, the overall performance has improved, in addition to saving time and effort to search for the appropriate parameters. Besides, the Spectral Information Divergence is used to replace the spectral distotion to evaluate the spectral similarity between two multispectral vectors. Results demonstrate that when the spectral information divergence and brightness criteria are utilized in the self-adaptive codebook method, the performance can be improved slightly even further on average. For the approach of fusion, two strategies, namely pooling and majority vote, are adopted to exploit benefits of each spectral band to obtain better object detection performance.