Pattern of life from WAMI objects tracking based on visual context-aware tracking and infusion network models

Abstract

With the emergence of long lasting surveillance systems, e.g., full motion video (FMV) networks and wide area motion imagery (WAMI) sensors, extracting targets’ long term pattern of life over a day becomes possible. In this paper, we present a framework for extracting the pattern of life (POL) of targets from WAMI video. We first apply a context-aware multi-target tracker (CAMT) to track multiple targets in the WAMI video and obtain the targets’ tracklets, traces, and the locations, from surveillance information extracted from the targets' long-term trajectories. Then, entity networks propagate over time are constructed with targets’ tracklets, traces, and the interested locations. Finally, the entity network is analyzed using network retrieving technique to extract the POL of interested targets.