Metadata extraction and correction for large-scale traffic surveillance videos

Metadata is widely used to facilitate user defined queries and high-level event recognition applications in traffic surveillance videos. Current metadata extraction approaches rely on some computer vision algorithms, which are not accurate enough in the real world traffic scenes, and do not deal with big surveillance data efficiently. In this paper, we design a novel metadata extraction and metadata correction system. Firstly, we define the structure of metadata to determine which attribute (e.g., vehicle enter time, license plate number, vehicle type) we need to extract. Based on this structure, we employ a three-phase method to extract metadata. Secondly, we propose a graph-based metadata correction approach for compensating the accuracy of metadata extraction method. It fuses the big metadata of whole camera network, automatically detects suspicious metadata and corrects them based on the metadata spatial-temporal relationship and the image similarity. As the centralized framework may not be able to cope with the huge amount of data generated by traffic surveillance system, our system is implemented in a distributed fashion using Hadoop and HBase. Finally, the experimental results on real world traffic surveillance videos demonstrate the efficiency of our system, and also demonstrate that the metadata quality is significantly improved after metadata correction.