We present an approach for reconfiguration of dynamic visual sensor networks with deep reinforcement learning (RL). Our RL agent uses a modified asynchronous advantage actor-critic framework and the recently proposed Relational Network module at the foundation of its network architecture. To address the issue of sample inefficiency in current approaches to model-free reinforcement learning, we train our system in an abstract simulation environment that represents inputs from a dynamic scene. Our system is validated using inputs from a real-world scenario and preexisting object detection and tracking algorithms.
In this paper we present a novel method for video cameras positioning and reconfiguration, to maximize visual coverage in complex indoor environments. Based on a suitable modeling of the camera field-of-view and of the environmental setup, the optimization procedure determines the most appropriate configuration of cameras to satisfy a coverage objective, taking into account a number of parameters on the quality of view at the camera position. This includes the global ground area coverage, the expected geometric distortion, and the entropy of the image. The proposed solution has been validated in different environmental setups, including synthetic settings, taking into account the presence of obstacles and constraints. In this paper, we present a hierarchical smart resource coordination and reconfiguration framework for distributed systems. We view the coordination problem as one of context aware resource reconfiguration. The fundamental unit in this hierarchy is a Fault Containment Unit (FCU) that provides run-time fault-tolerance by deciding on the best alternative course of action when a failure occurs. FCUs are composed hierarchically and are responsible for dynamically reconfiguring failing FCUs at lower levels. When such a reconfiguration is not possible, FCUs propagate the failure upward for resolution. We evaluate the effectiveness of our framework in a people tracking application using a network of cameras. The task for our multi-camera network is to allocate pairs of cameras that localize a subject optimally given the current run-time context. The system automatically derives policies for switching between camera pairs that enable robust tracking while being attentive to certain performance measures. Our approach is unique in that we model the dynamics in the scene and the camera network configuration steers the policies to provide robust tracking.