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Characterized by their distinctive motion patterns, temporal textures are natural phenomenon exhibiting spatio-temporal regularity with indeterminate spatial and temporal extent. This paper presents a real-time motion-based temporal texture characterization technique for the first time using block-based motion measures with very high classification accuracy against the popular opinion that such an accurate characterization is only possible using pixel-based measures. Finding an optimal weight ratio between space and time domain features where the accuracy of this block-based technique peaks has been the essence of this success. Computational complexity analyses and classification results clearly demonstrate the capability of the proposed technique in producing comprehensive classification results comparable to the best pixel-based technique with overwhelming reduction in computational complexity.