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First-person action recognition is a recent problem in computer vision, where an observer wears body cameras to understand and recognize actions from the captured video sequences. Technological advances have made it possible to offer small wearable cameras that can be attached onto bike helmets, belts, animal halters, among other accessories. Examples of potential applications include sports, security, healthcare, visual lifelogging, among others.

In this paper, we exploit deep convolutional features for object appearance modeling and propose a simple while effective deep iscriminative model (DDM) for visual tracking. The proposed DDM takes as input the deep features and outputs an object-background confidence map. Considering that both spatial information from lower convolutional layers and semantic information from higher layers benefit object tracking, we construct multiple deep discriminative models (DDMs) for each layer and combine these confidence maps from each layer to obtain the final object-background confidence map.

Segment-tree (ST) based cost aggregation algorithm for stereo matching successfully integrates the information of segmentation with non-local cost aggregation framework. The tree structure which is generated by the segmentation strategy directly determines the final results for this kind of algorithms. However, the original strategy performs unrea-sonable due to its coarse performance and ignores to meet the disparity consistency assumption.