Target Detection and Pedestrian Recognition in Infrared Images

By improving the local contrast between targets and background in the static infrared images, a simple and effective background model is proposed to detect targets. At the same time, a novel learning algorithm is presented for training a discriminatively trained, part-based model with only positives images, for pedestrian recognition. The background models are constructed based on the static infrared images by morphological operations. Meanwhile, the learning algorithm is based on the ramp loss function, which can filter out the false negatives from the collected negative examples. It has a great advantage on training the deformable part models with latent variables when the dataset has a large number of noisy examples. Experiments manifest that our background model can achieve a high precision in target detection and the discriminative part model trained by the proposed learning approach can recognize the targets well and truly, with the help of target detection.