In recent years, we have witnessed the widespread application of deep-learning techniques to various surveillance tasks, including human tracking and counting, abnormal behavior detection, and video segmentation. In most cases, the input images/videos are assumed to possess adequate visual quality to guarantee satisfactory performance. However, accuracy may be adversely affected when the input data are degraded by factors such as excessive noise or poor lighting conditions. In the paper, we develop a deep neural network based on the U-Net architecture that acts as a pre-processing module to restore images/videos with nonuniform light sources to ensure the accuracy of the subsequent object detection process. Experimental results on the VisDrone20 19 dataset  demonstrate the effectiveness of the proposed method, achieving a remarkable 5% increase in average recall. We expect the framework to be universally applicable to situations that call for the enhancement of raw input data.
2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance, University of Taipei