EV-Matching: Bridging Large Visual Data and Electronic Data for Efficient Surveillance

Visual (V) surveillance systems are extensively deployed and becoming the largest source of big data. On the other hand, electronic (E) data also plays an important role in surveillance and its amount increases explosively with the ubiquity of mobile devices. One of the major problems in surveillance is to determine human objects' identities among different surveillance scenes. Traditional way of processing big V and E datasets separately does not serve the purpose well because V data and E data are imperfect alone for information gathering and retrieval. Matching human objects in the two datasets can merge the good of the two for efficient large-scale surveillance. Yet such matching across two heterogeneous big datasets is challenging. In this paper, we propose an efficient set of parallel algorithms, called EV-Matching, to bridge big E and V data. We match E and V data based on their spatiotemporal correlation. The EV-Matching algorithms are implemented on Apache Spark to further accelerate the whole procedure. We conduct extensive experiments on a large synthetic dataset under different settings. Results demonstrate the feasibility and efficiency of our proposed algorithms.