【DeepLearning】簡述Visual Tracking with Fully Convolutional Networks
Visual Tracking with Fully Convolutional Networks 背景 研究意義 整體結構 算法實現 實驗結果 一、背景： 1.現有的跟蹤方法多數採用生成或區分模型，並將不同類別的但同時發生的對象分開。 2.它們依賴於低級別的手工特徵，這些特徵不能捕捉到目標的語義信息，對外觀顯著變化的物體效果不好，只有有限的區分能力。 3.在處理重大外觀變化，姿態變化，嚴重遮擋和
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相關文章
- 1. Fully-Convolutional Siamese Networks for Object Tracking
- 2. visual tracking 經典文獻翻譯（1）- Fully-Convolutional Siamese Networks for Object Tracking
- 3. Fast Image Processing with Fully-Convolutional Networks
- 4. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
- 5. Fully Convolutional Networks
- 6. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
- 7. Automatic skin lesion segmentation with fully convolutional-deconvolutional networks
- 8. Multi-Oriented Text Detection with Fully Convolutional Networks
- 9. Deeper Depth Prediction with Fully Convolutional Residual Networks
- 10. 【Tracking 系列：一】Siamese-FC｜Fully-Convolutional Siamese Networks for Object Tracking