Video motion analysis and its applications have been a classic research topic for decades. In this paper, we explore the problem of real time video semantics understanding based on motion information. The work can be divided into two segments: global / camera motion estimation and object motion analysis. The former involves optical flow analysis and semantic meaning parsing, and the latter involves object detection and tracking. Although each of these topics has been studied extensively in the literature, a thorough system combining all of them without human intervention, especially under a real time application scenario, is still worthy of further investigation. In this paper we develop our approach toward such a destination and propose an integral architecture. The usability and efficiency of the proposed system have been demonstrated through experiments. Results of this project have numerous applications in digital entertainment, such as video and image summarization, annotation, retrieval and editing.
Recently we have witnessed a growing interest in the development of the subband/wavelet coding (SBC) technology, partly due to the superior scalability of SBC. Scalable coding provides great synergy with the universal media access applications, where media content is delivered to client devices of diverse types through heterogeneous channels. In this respect, SBC system provides flexibility in realizing different ways of media scaling, including scaling dimensions of SNR, spatial, and temporal. However, the selection of specific scalability operations given the bit rate constraint has always been ad hoc - a systematic methodology is missing. In this paper, we address this open issue by applying our content-based optimal scalability selection framework and adopting subjective quality evaluation. For this purpose we firstly explore the behavior of SNR-Spatial-Temporal scalability using Motion Compensated (MC) SBC systems. Based on the system behavior, we propose an efficient method for the optimal selection of scalability operator through content-based prediction. Our experiment results demonstrate that the proposed method can efficiently predict the optimal scalability operation with an excellent accuracy.