The complicated and high-dimensionality features are always employed in object detection for improving the detection accuracy. Higher dimensionality features often yield higher computational complexity and memory cost. Therefore, feature compression algorithms are applied for reducing the dimensionality. The classical feature compression algorithms such as principal component analysis (PCA) and singular value decomposition (SVD) involve a large number of matrix decomposition operations, which are inefficient. To address this problem, a feature compression algorithm based on random projection was proposed. In this algorithm, the high-dimensionality feature was mapped into the low-dimensionality feature space by a sparse random matrix and efficient matrix multiplication. The compressed feature vectors were applied to build an Ada-Boost classifier, and the experimental results show that the detector not only guarantees the detection accuracy but also improves the detection speed.
