A Target Tracking Algorithm Based on CNN for High-speed Calculation of Hardware

To achieve the specified target tracking on the hardware. A target tracking algorithm based on convolution neural network for hardware is proposed. By analyzing the effects of the coiling layer, convolution kernel, subsampling layer, and activation layer on network performance, various CNN structures are constructed for hardware implementation. By training the target sample, the target model based on the convolution depth characteristics is obtained. The optimized model parameters are invoked to track the target tracking on the hardware, by using a flexible search strategy. The performance and tracking effect of a variety of networks are compared with an example. The parameter size of the optimal model is 368Byte, and the test error rate is 0.0125, and the mean of tracking error is 0.779. The effectiveness and feasibility of the algorithm to achieve target tracking on hardware is proved.