Image Feature Fusion for Human Detection with Multi-Sensor Based on FHOG

Proposed a novel image feature fusion approach based on histogram of oriented gradient (HOG). The visual activation measure (VAM) was used to select the statistics of local gradients with significant direction, and forms fused histogram of gradient (FHOG), which effectively solved the existing deficiencies of multi-resolution (MR) image fusion. These fused features were plugged into support vector machine (SVM), and train human/background binary classifier for human detection. Experiments show that compared with the traditional MR image fusion approaches, in reference points the missing rate of the proposed approach decreases by 3%～10%, and the false alarm rate drops by an average of more than 20%.