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Feature-level image sequence fusion based on histograms of oriented gradients.
| Content Provider | CiteSeerX |
|---|---|
| Author | Wang, Meng Dai, Yaping Liu, Yan Yanbing, Tian |
| Abstract | Abstract—For improving accuracy and robust property of human detection, fusion of image sequences captured from visible-thermal sensors is lucrative. Instead of performing it in pixel-level directly, we try to fuse object features by a novel image sequence fusion algorithm based on gradient feature (GFIF). The GFIF algorithm calculate gradients of input images to form a joint histograms of Oriented Gradient (HOG) descriptor, and these fused features are used to train linear support vector machine (SVM). In our experiment, a color-thermal surveillance dataset is adopted and several multi-resolution fusion algorithms are tested for comparing. By the Detection Error Tradeoff (DET) curves, the GFIF algorithm shows its superiority. Keywords—Object detection; SVM; Histogram of Oriented Gradient (HOG); multi-resolution image fusion; feature-level |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Oriented Gradient Feature-level Image Sequence Fusion Object Feature Several Multi-resolution Fusion Algorithm Linear Support Vector Machine Detection Error Tradeoff Novel Image Sequence Fusion Algorithm Keywords Object Detection Gfif Algorithm Gfif Algorithm Calculate Gradient Human Detection Image Sequence Multi-resolution Image Fusion Input Image Visible-thermal Sensor Gradient Feature Color-thermal Surveillance Dataset Robust Property Joint Histogram |
| Content Type | Text |
