A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results.
Third, the generalization of the proposed method is validated on various tracking datasets as well as CNN models with similar architectures.
To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy.
In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking.
In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising.
Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.
Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized.
It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB.