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The surveillance video summarization system is an important technical tool used to extract key information from large and complex surveillance videos, providing effective support for security management and event analysis. Traditional manual summarization methods have become inadequate in meeting the demands of rapid processing and accurate extraction of necessary information due to the proliferation of surveillance devices and the rapid growth of surveillance video data. Modern deep learning methods commonly suffer from high computational complexity and a large number of parameters. To address this issue, a dynamic Transformer-based surveillance video summarization model is proposed.The model automatically assigns an appropriate number of tokens to each input video frame, cascades multiple Transformer models, and gradually increases the number of generated tokens to achieve adaptive activation order. Once sufficiently confident predictions are made, the inference process terminates. The model employs feature reuse and attention reuse techniques to reduce redundant computations. It has made significant progress in reducing computational complexity.Experimental tests show that compared to traditional models, the dynamic Transformer model achieves improvements in accuracy, with score metrics increasing by 3.7% and 0.9% on two publicly available datasets, respectively. At the same time, the computational complexity is reduced by 40%. This model can meet the precision requirements and surveillance demands, demonstrating good generalization performance.