This article addresses the research problem of how to autonomously control Pan/Tilt/Zoom (PTZ) cameras in a manner that seeks to optimize the face recognition accuracy or the overall threat detection and proposes an overall system. The article presents two alternative schemes for camera scheduling: Grid-Based Grouping (GBG) and Elevator-Based Planning (EBP). The camera control works with realistic 3D environments and considers many factors, including the direction of the subject’s movement and its location, distances from the cameras, occlusion, overall recognition probability so far, and the expected time to leave the site, as well as the movements of cameras and their capabilities and limitations. In addition, the article utilizes clustering to group subjects, thereby enabling the system to focus on the areas that are more densely populated. Moreover, it proposes a dynamic mechanism for controlling the pre-recording time spent on running the solution. Furthermore, it develops a parallel algorithm, allowing the most time-consuming phases to be parallelized, and thus run efficiently by the centralized parallel processing subsystem. We analyze through simulation the effectiveness of the overall solution, including the clustering approach, scheduling alternatives, dynamic mechanism, and parallel implementation in terms of overall recognition probability and the running time of the solution, considering the impacts of numerous parameters.

The efficient control of Pan/Tilt/Zoom (PTZ) cameras has been a major research problem. This paper presents a solution that seeks to optimize the overall subject recognition probability by controlling various deployed cameras, based on the characteristics of the subjects in the surveillance area. In particular, we propose and analyze a clustering-based approach, which can be used in conjunction with recently proposed camera scheduling schemes, to achieve significant improvements in both the subject recognition probability and the algorithm computation time. We extensively analyze the effectiveness of the clustering approach, considering the impacts of subject arrival rate.

This paper deals with the camera control problem in automated video surveillance. We develop a solution that seeks to optimize the overall subject recognition probability by controlling the pan, tilt, and zoom of various deployed Pan/Tilt/Zoom (PTZ) cameras. Since the number of subjects is usually much larger than the number of video cameras, the problem to be addressed is how to assign subjects to these cameras. This control of cameras is based on the direction of the subject's movement and its location, distances from the cameras, occlusion, overall recognition probability so far, and the expected time to leave the site, as well as the movements of cameras and their capabilities and limitations. The developed solution works with realistic 3D environments and not just 2D scenes. We analyze the effectiveness of the proposed solution through extensive simulation.

This paper considers scalable delivery of streaming video content with advertisements. It proposes a highly accurate waiting-time prediction algorithm that estimates the expected number of viewed ads by utilizing detailed information about the system state and the applied scheduling policy. It also proposes a pricing scheme based on the expected waiting times, which include the ads' viewing times. The revenues generated by the ads are used to subsidize the price and are allocated to clients proportionally to their expected waiting times. The envisioned system presents the client with an updated menu of the supported videos (such as full-length featured movies) along with the corresponding ads' viewing times and prices. We analyze the effectiveness of the proposed prediction algorithm and pricing scheme in terms of many metrics, considering numerous design and workload parameters, and capturing purchasing capacity and willingness models.

Providing video streaming users with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait. In the absence of any waiting-time feedback, users are more likely to defect because of the uncertainty as to when their services will start. We analyze waiting-time predictability in scalable video streaming. We propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits.

This paper presents a delivery framework for streaming media with advertisements and an associated pricing model. The delivery model combines the benefits of periodic broadcasting and stream merging. The advertisements' revenues are used to subsidize the price of the media content. The pricing is determined based on the total ads' viewing time. Moreover, this paper presents three modified scheduling policies that are well suited to the proposed delivery framework and analyzes their effectiveness through simulation.

Providing video streaming users with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait. In the absence of any waiting-time feedback, users are more likely to defect because of the uncertainty as to when they will start to receive services. In this paper, we analyze waiting-time predictability in scalable video streaming. We present three prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits.