A Distributed Crawler for IoVT-based Public Safety Surveillance Exploring the Spatio-Temporal Correlation

Modern infrastructure development has led to a rise in deployed surveillance cameras to monitor remote locations and widespread infrastructures. In today's networked surveillance environment, however, human operators are often overwhelmed with the huge amount of visual feeds, which causes poor judgment and delayed response to emergencies. This paper proposes a distributed crawler scheme (DiCrawler) for smart surveillance systems deployed on Internet of Video Things (IoVT). The IoVT camera nodes monitor continuous video input, track the object of interest while preserving privacy, and relay correlative information to targeted nodes for constant monitoring. Each IoVT node monitors the space inside its field of view (FoV) and notifies the neighboring nodes about the objects leaving the FoV and heading in their directions. A smart communication algorithm among IoVT nodes is designed to prevent network bandwidth bottlenecks and preserve computational power. The DiCrawler system can corroborate with human operators and assist with decision-making by raising alarms in case of suspicious behavior. The IoVT network is completely decentralized, using only peer-to-peer (P2P) communication. DiCrawler does not rely on a central server for any computations, preventing a potential bottleneck if hundreds of cameras were connected and constantly uploading data to a server. Each module is also in a compact form factor, making it viable to be mounted on traditional security surveillance cameras. Extensive experimental study on a proof-of-concept prototype validated the effectiveness of the DiCrawler design.