원문정보
초록
영어
Recently, the compressive tracking (CT) method has attracted much attention due to its
high efficiency. However, the CT extracts samples around the previous target region
within a fixed search radius; the searching area is unsuitable when the target undergoes
abrupt acceleration change. Meanwhile, the classifier learns the features of the target
online without judgment even the target is fully occluded. Thus, the improper searching
area and incorrectly updated features lead to a marked drop in precision of tracking. To
solve this issue, a robust target tracking method integrating spatio-temporal model to
constrain the searching area is proposed in this paper. Different from CT, the proposed
method initially constructs the spatio-temporal model to calculate a confidence map
between consecutive frames, and the region with high confidence suggests the high
possibility that target exists. Thus the samples can be extracted in the high confidence
area. Then, the optimal target location can be estimated with a naive Bayes classifier
using sparse coding features. Experiments show that the proposed method outperforms
several competing methods in efficiency and robustness.
목차
1. Introduction
2. Related Work
3. Compressive Tracking
3.1. Compressive Sensing
3.2. Online Classifier Update
4. Proposed Algorithm
4.1. Spatial Relationship Model
4.2. Spatio-Temporal Model
4.3. Update Searching Area
5. Experiments
5.1. Experimental Setup
5.2. Experimental Results
6. Conclusion
References