In order to solve the problem of inaccurate positioning in the process of target tracking due to illumination, scale change and occlusion, a correlation filtering tracking algorithm based on generate compression network is proposed. Firstly, the encoder is used to compress the high-dimensional depth features extracted from VGG16 network, and the soft quantizer reduces floating-point operation so that improves the operation speed. Secondly, the discriminator of generative adversarial network guides the encoder and generator to better compress and recover the original depth features by generating a discriminator against the network, so as to enhance the ability of the encoder to extract the key features of the target, then, the compressed depth features, gray features and hog features are combined to improve the ability of target representation. Thirdly, the correlation filter and PCA feature dimensionality reduction are used to complete the target precise location and discriminant scale estimation. The experimental results show that the tracking accuracy of the proposed algorithm is 87.1%, and the tracking rate is up to 70fps.

为了解决目标跟踪过程中由于光照、尺度变化和遮挡等原因导致定位不准确的问题，提出了一种基于生成压缩网络的相关滤波跟踪算法。首先使用编码器对从VGG16网络中提取的高维深度特征进行压缩，软量化器减少浮点运算，提高运算速度。其次，生成对抗网络的判别器通过对网络生成判别器来引导编码器和生成器更好地压缩和恢复原始深度特征，从而增强编码器提取目标关键特征的能力，然后，将压缩后的深度特征、灰度特征和猪特征结合起来，提高了目标表示的能力。第三，利用相关滤波器和PCA特征降维完成目标精确定位和判别尺度估计。实验结果表明，所提算法的跟踪精度为87.1%，跟踪率高达70fps。