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This paper presents an approach for recognizing human activities from extremelow resolution (e.g., 16x12) videos. Extreme low resolution recognition is notonly necessary for analyzing actions at a distance but also is crucial forenabling privacy-preserving recognition of human activities. We design a newtwo-stream multi-Siamese convolutional neural network. The idea is toexplicitly capture the inherent property of low resolution (LR) videos that twoimages originated from the exact same scene often have totally different pixelvalues depending on their LR transformations. Our approach learns the sharedembedding space that maps LR videos with the same content to the same locationregardless of their transformations. We experimentally confirm that ourapproach of jointly learning such transform robust LR video representation andthe classifier outperforms the previous state-of-the-art low resolutionrecognition approaches on two public standard datasets by a meaningful margin.

Extreme Low Resolution Activity Recognition with Multi-Siamese Embedding Learning