Vision through distortions: Atmospheric Turbulence-and Clothing-invariant long-range recognition

Long-range recognition is paramount in securitysensitive settings. It faces the hard task of retrieving images from a high-resolution gallery given a probe image affected by distortions due to atmospheric turbulence and different features, such as clothing. This work proposes a novel atmospheric turbulenceand clothing-invariant whole-body model to address the longrange recognition task. It leverages self-defined proxies across different acquisition ranges, a novel way to create diverse batches based on capturing condition and clothing, and a condition- and clothing-aware loss function. As most whole-body benchmarks have limited ranges, we employ the BRIAR dataset for training and evaluation. It comprises identities captured within 100 to 1,000 meters from the camera in various poses, lighting conditions, and clothing variations. Quantitative and qualitative analysis show our model leads to distortion-invariant discriminative features across different recording capturing ranges. It also obtains competitive performance compared to the state-of-the-art benchmarks Market1501, MSMT17, and DeepChange.

Conference paper