Traditional discriminant correlation filters fuse hand-crafted features and deep features fusion for tracking, which will cause features redundancy too much and lead to model overfitting when parameters are updated. Moreover, it is difficult to deal with various complex problems in video by using the same label map for different features. In addition, the model updating strategy of the traditional method is relatively single, the model is updated by one frame or several frames. Different from the traditional methods of response map fusion, this paper proposes a multi-layer features correlation filter algorithm to estimate the target model from multiple perspectives. The corresponding label maps are used for different features, and an adaptive model updating strategy is proposed. The proposed tracker achieving leading performance in OTB2013, OTB2015 and VOT2016 datasets.
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