Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio value of EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations from to ,) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.
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