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Event-driven video adaptation: A powerful tool for industrial video supervision

机译:事件驱动的视频自适应:工业视频监控的强大工具

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摘要

Efficient video content adaptation requires techniques for content analysis and understanding as well as the development of appropriate mechanisms for content scaling in terms of the network properties, terminal devices characteristics and users' preferences. This is particularly evident in industrial surveillance applications, due to the huge amount of data needed to be stored, delivered and handled. In this paper, we address both issues by incorporating (a) computer vision tools that allows efficient tracking of salient visual objects for long time regardless of the dynamics of the visual environment -via a self initialized tracking algorithm-and (b) an adaptive optimal rate distortion scheme able to allocate different priorities for each detected video object with respect to users' needs, network platforms capabilities and terminal characteristics. The self initialized tracker firstly appropriately describes visual content, secondly incorporates adaptive mechanisms for automatically update the tracker to adjust to the current conditions and thirdly includes an efficient decision mechanism that estimates the time instances in which adaptation should be activated. For the rate distortion algorithm, an optimal adaptive framework is adopted which is capable of allocating the desired quality to objects of users' interest without violating the target bit rate of the sequence. The Wavelet Packet Transform (WPT) is adopted towards this purpose. The advantage of the WPT is that it localizes the frequency components of each video object and therefore it offers additionally content adaptability according to video object texture coding. The WPT tree is transmitted only at the first frame of each shot and thus dew bits are required for its encoding. Experimental results and comparisons with other approaches are presented to illustrate the good performance of the proposed architecture. The results cover real-world and complex industrial environments.
机译:有效的视频内容适配需要用于内容分析和理解的技术,以及用于根据网络属性,终端设备特性和用户偏好来进行适当的内容缩放机制的开发。由于需要存储,传递和处理大量数据,因此在工业监控应用中尤其明显。在本文中,我们通过合并(a)计算机视觉工具来解决这两个问题,该工具可通过自初始化跟踪算法,不管视觉环境如何动态地长时间有效地跟踪显着的视觉对象,以及(b)自适应最优速率失真方案,能够根据用户需求,网络平台功能和终端特性为每个检测到的视频对象分配不同的优先级。自初始化跟踪器首先适当地描述视觉内容,其次包括用于自动更新跟踪器以适应当前条件的自适应机制,其次包括有效的决策机制,该机制估计应激活自适应的时间实例。对于速率失真算法,采用了一种最佳的自适应框架,该框架能够在不违反序列目标比特率的情况下,将所需质量分配给用户感兴趣的对象。为此目的采用了小波包变换(WPT)。 WPT的优点是它可以定位每个视频对象的频率分量,因此可以根据视频对象纹理编码提供额外的内容适应性。 WPT树仅在每个镜头的第一帧发送,因此需要露水位进行编码。实验结果和与其他方法的比较表明了所提出的体系结构的良好性能。结果涵盖了现实世界和复杂的工业环境。

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