| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 106 === Recently, the number of neural networks researches on reducing storage space and computational resources has been increasing, and these studies have made great progress from theory to practice. This paper focuses on the high similarity which exists in the scene of a surveillance video and presents a method to optimize convolutional neural networks, called dynamic convolution. Compared with the existing technology, this method can directly be applied to the existing convolution neural network architecture without retraining and analyzing weights, and effectively reduce the computation of convolution. The experiment tests 14 surveillance videos with various scenes. This paper show that proposed method can reduce inference costs for detecting objects (Single Shot MultiBox Detector[21]) by up to 39% of FLOP while the effect of accuracy is lower than 0.7% mAP, the dynamic convolution proposed in this paper optimize convolutional neural networks in a specific aspect of the convolution process which is different from the existing acceleration methods. Therefore, the proposed method can be complementary to existing acceleration methods to further speed up the performance.
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