Automatic annotation of pedestrians in thermal images using background/foreground segmentation for training deep neural networks

Abstract

Deep Neural Networks for object detection have become of significant interest with the substantial improvement in their efficiency and increase in their applications. However, training such a network requires a large annotated dataset, which is very expensive in term of time and human effort. In this context, several automatic image annotation solutions have been proposed which are dependent on rich visual features and thus suitable for color images only. On the other hand, recent experiments have proven that color cameras alone are not enough for pedestrian related applications and there is a need to use other visual sensors as well, such as thermal cameras. In this paper, we propose an automatic image annotation technique for pedestrian detection in thermal images using an adaptive background/foreground estimation model to train Faster-RCNN. The results presented in this paper, obtained through long-term experiments demonstrate the efficacy of our technique. The results also show that our proposed technique is be very useful to generate image annotations automatically and to train a deep neural network without having a manually annotated dataset for cameras with different modalities.