Extreme Low Resolution Activity Recognition with Spatial-Temporal Attention Transfer
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Extreme low-resolution(LR) activity recognition plays a vital role in privacy protection. In the meantime, remote target recognition is also critical, especially in surveillance cameras. In this problem, the information capacity of LR data is relatively rare. How to exploit high-resolution(HR) data for improving the accuracy of LR action recognition is a notable issue. In this work, we make full use of the HR information of separate spatial and temporal features to promote LR recognition by acquiring better attention. Experiments show that our proposed method can improve LR recognition accuracy up to 4.4%. Moreover, related experiments are implemented in the well-known datasets (e.g. UCF101 and HMDB51). The results achieve state-of-the-art performance on 12*16 HMDB51.
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