STTR

To fully exploit the capabilities of sensors in real life, especially cameras, smart camera surveillance requires the cooperation from both domain experts in computer vision and systems. Existing alert-based smart surveillance is only capable of tracking a limited number of suspicious objects, while in most real-life applications, we often do not know the perpetrator ahead of time for tracking their activities in advance. In this work, we propose a radically different approach to smart surveillance for vehicle tracking. Specifically, we explore a smart camera surveillance system aimed at tracking all vehicles in real time. The insight is not to store the raw videos, but to store the space-time trajectories of the vehicles. Since vehicle tracking is a continuous and geo-distributed task, we assume a geo-distributed Fog computing infrastructure as the execution platform for our system. To bound the storage space for storing the trajectories on each Fog node (serving the computational needs of a camera), we focus on the activities of vehicles in the vicinity of a given camera in a specific geographic region instead of the time dimension, and the fact that every vehicle has a "finite" lifetime. To bound the computational and network communication requirements for detection, re-identification, and inter-node communication, we propose novel techniques, namely, forward and backward propagation that reduces the latency for the operations and the communication overhead. STTR is a system for smart surveillance that we have built embodying these ideas. For evaluation, we develop a toolkit upon SUMO to emulate camera detections from traffic flow and adopt MaxiNet to emulate the fog computing infrastructure on Microsoft Azure.