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The authors present a feedback-based approach for event detection in video surveillance that improves the detection accuracy and dynamically adapts the computational effort depending on the complexity of the analysed data. A core feedback structure is proposed based on defining different levels of detail for the analysis performed and estimating the complexity of the data being analysed. Then, three feedback-based analysis strategies are defined (based on this core structure) and introduced in the processing stages of a typical video surveillance system. A rule-based system is designed to manage the interaction between these feedback-strategies. Experimental results show that the proposed approach slightly increases the detection reliability, whereas highly reduces the computational effort as compared to the initially developed surveillance system (without feedback strategies) across a variety of multiple video surveillance scenarios operating at real time.