Evaluation of tone mapping and exposure fusion algorithms on HDR videos for face detection and recognition

Çavdarlı, Fehime Betül
High dynamic range (HDR) images have become popular recently especially for video surveillance systems. One of the most important reasons for this is that in areas where there is under or over-exposure, classical low dynamic range (LDR) images are insufficient to capture details, while HDR images have better visual details and contain wide range illumination values. However, since HDR images cannot be viewed on conventional LDR displays, additional processing such as tonemapping and/or fusion are required to convert HDR images to LDR images without losing the details. In this thesis, sequential video frames with 3 different exposure times are obtained with an HDR-capable camera. After HDR images are obtained from these images they are deghosted using a state-of-the-art deghosting algorithm to minimize visual artifacts due to object motion. Then 34 different tone mapping operators (TMO) and 20 different exposure fusion algorithms (EFA) have been applied to deghosted video frames. The face detection performance of these algorithms is evaluated by calculating mAP and the performance in terms of face recognition is evaluated by calculating precision, recall, and F1-score with different averaging methods. The results suggest that images obtained with HDR imaging algorithms improve face detection and, in particular, face recognition results, however there are noticeable differences between the performances of different algorithms. As such our study sheds light on which HDR algorithms are more suitable for using in a video surveillance scenario if the goal is to improve the performance of face detection and recognition. We also make available a public dataset as well as a labeling software to stimulate further research in this direction.


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Citation Formats
F. B. Çavdarlı, “Evaluation of tone mapping and exposure fusion algorithms on HDR videos for face detection and recognition,” M.S. - Master of Science, Middle East Technical University, 2022.