Accurate Image-Based Pedestrian Detection With Privacy Preservation

In this paper, we propose an accurate pedestrian detection scheme with privacy preservation (PPPD) based on pedestrian images. By utilizing the vector homomorphic encryption (VHE), private linear-transforming matrices are ingeniously designed to enable arbitrary permutation operations for the encrypted vectors. In this way, the feature vector extraction of the histogram of oriented gradient (HOG) can be efficiently performed over the encrypted pedestrian images. Furthermore, due to the encrypted inner product calculations supported by VHE, an encrypted kernel matrix is constructed to generate multiple encrypted kernels (i.e., linear, polynomial, and Gaussian kernels). The pedestrian detection model based on the supported vector machine (SVM) can be securely trained over the encrypted kernels. With the proposed scheme, the extracted features of pedestrian images are not necessary to be returned to the image owner for decryption, such that the communication costs can be significantly reduced. In addition, the privacy of the whole process in pedestrian detection can also be guaranteed. Extensive experiments are conducted over multiple pedestrian datasets, and it is demonstrated that PPPD can achieve high accuracy of pedestrian detection with lower computation and communication overhead compared with the existing schemes.