Multiple Scaled Person Re-Identification Framework for HD Video Surveillance Application

Person re-identification is an important problem in automated video surveillance. It remains challenging in terms of extraction of reliable and distinctive features, and matching of the features under different camera views. In this paper, we propose a novel re-identification strategy for person re-identification based on multiple image scaled framework. Specifically, global features and local features are extracted separately in different image scales. These two-scaled processing are constructed in a cascaded system. We use semi-supervised SVM to obtain a similarity function for global features and a similarity function combining the spatial constraint and salience weight for local features. Experiments are conducted on two datasets: ETHZ and our dataset with high resolution. Experimental results demonstrate that the proposed method outperforms the conventional method in terms of both accuracy and efficiency.