In this paper we propose a novel and systematic approach for dynamic allocation of tasks in a video surveillance system using smart cameras and based on Cloud/Fog architecture. Tracking tasks arrive in the system in a random way and must be assigned to the available devices (cameras, Fog nodes and the Cloud). Our approach guarantees the best solution optimizing power consumption and communication cost over the system. The proposed methods uses an integer programming model and its effectiveness is shown on an application example.

Video processing applications are becoming more complex and greedy in terms of computing resources. Thus, the designers of video surveillance systems are moving more and more towards distributed systems, comprising several video sensors collaboratively working to carry out tracking tasks in particular. However, there is a plethora of collaborative tracking algorithms, in the literature, each with its own advantages and disadvantages. The purpose of this paper is to present the most common collaborative tracking algorithms and discuss the strengths and weaknesses of each.