Reconstruction error based pedestrian detection in infrared videos

Pedestrian detection in infrared videos is a task full of potential, and has gotten more and more attention. To robustly detect the pedestrians in infrared image, a PCA-based detecting framework is designed in this paper. The proposed pedestrian detection system can be divided into two parts: training and classification. When the training stage is running, PCA is performed on two different datasets, pedestrian (positive) samples and non-pedestrian (negative) samples, separately. In the classification stage, the system determine whether the input candidate images belong to the positive samples or not by calculating the reconstruction errors for each of them based on the eigenvectors of positive sample and negative sample space. To improve the detecting performance, both the grayscale and edge descriptors are used in the training step. Experimental results indicated that PCA with the combination of grayscale and edge images could achieve the best performance for pedestrian detection.