Enabling Enriched TV Shopping Experience via Computational and Temporal Aware View-Centric Multimedia Abstraction

Smart TVs have realized the convergence of TV, Internet , and PC technologies, but still do not provide a seamless content interaction for TV-enabled shopping. To purchase interesting items displayed in a TV show, consumers must resort to a store or the Web, which is an inconvenient way of purchasing products. The fundamental challenge in realizing such a use case consists of understanding the multimedia content being streamed. Such a challenge can be realized by utilizing object detection to facilitate content understanding though it has to be executed as a computationally bound process so that consumers are provided with a responsive and exciting user interface. To this end, we propose a computational- and temporal-aware multimedia abstraction framework that facilitates the efficient execution of object detection tasks. Given computational and temporal rate constraints, the proposed framework selects the optimal video frames that best represent the video content and allows the execution of the object detection task as a computationally bound process. In this sense, the framework is computationally scalable as it can adapt to the given constraints and generate optimal abstraction results accordingly. Additionally, the framework utilizes "object views" as the basis for the frame selection process, which depict salient information and are represented as regions of interest (ROI). In general, an ROI can be a whole frame or a region that discards background information. Experimental results demonstrate the computational scalability of the proposed framework and the benefits of using the regions of interest as the basis of the abstraction process.