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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.

In this paper, we propose a new characteristic measure for relative people density and motion dynamics for the purpose of long-term crowd monitoring. While many related works focus on direct people counting and absolute density estimation, we will show that relative densities provide reliable information on crowd behaviour. Furthermore, we will discuss the derivation of a so-called Congestion Level of local areas in the crowd, which takes the current dynamics and density within a certain image region into account.

Numerous trackers have been proposed in recent years with considerable success. But few trackers can cope with all scenarios without failures. It is very difficult to design a tracker robust enough to keep off tracking failure. As failure is inevitable, we propose a framework to correct tracker, verify failure, predict object position and re-detect object. The original model of the first frame is used to correct the tracker. Then, the confidence of tracking is used to verify tracking failure.