Context-aware graph modeling for object search and retrieval in a wide area camera network
This paper addresses the problem of context-aware object search and retrieval in a wide area distributed camera network. With the proliferation of smart cameras in urban networks, it is a challenge to process this big data in an efficient manner. A novel graph based model is proposed to represent relationships, and for search and retrieval tasks. This representation exploits the fact that objects occurring in close spatial-temporal proximity are not completely independent and serve as context for each other. Additional information such as appearance and scene context can also be encoded into the graph model to improve the overall accuracy. A manifold ranking strategy is used to order the items based on similarity with an emphasis on diversity. Extensive experimental results on a ten camera network are presented.