Object detection using discriminative photogrammetric context

Photogrammetric context captures the relationship between object heights and camera viewpoint, and can be used to reject false detections that appear in wrong locations or scales. In this work, we address the problem of using photogrammetric constraints in object detection when camera poses are unknown. We propose a model to capture both local appearance features and global photogrammetric context, in which the camera pose is treated as a latent variable. We use latent Structural SVM to learn the model parameters. To solve the NP-hard problem in structured prediction, we propose a branch-bound-and-cut algorithm, where cuts of the latent variable are embedded into a branch-and-bound process. The model is experimentally evaluated on INRIA pedestrian dataset. The results show that our model can get significantly better detection performance than models using only appearance features or using photogrammetric context in a graphical model.