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A Real-Time Feature Indexing System on Live Video Streams

机译：实时视频流上的实时特征索引系统

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Most of the existing video storage systems rely on offline processing to support the feature-based indexing on video streams. The feature-based indexing technique provides an effective way for users to search video content through visual features, such as object categories (e.g., cars and persons). However, due to the reliance on offline processing, video streams along with their captured features cannot be searchable immediately after video streams are recorded. According to our investigation, buffering and storing live video steams are more time-consuming than the YOLO v3 object detector. Such observation motivates us to propose a real-time feature indexing (RTFI) system to enable instantaneous feature-based indexing on live video streams after video streams are captured and processed through object detectors. RTFI achieves its real-time goal via incorporating the novel design of metadata structure and data placement, the capability of modern object detector (i.e., YOLO v3), and the deduplication techniques to avoid storing repetitive video content. Notably, RTFI is the first system design for realizing real-time feature-based indexing on live video streams. RTFI is implemented on a Linux server and can improve the system throughput by upto 10.60x, compared with the base system without the proposed design. In addition, RTFI is able to make the video content searchable within 20 milliseconds for 10 live video streams after the video content is received by the proposed system, excluding the network transfer latency.
机译：大多数现有的视频存储系统都依赖于脱机处理来支持基于特征的视频流索引。基于特征的索引技术为用户提供了一种通过视觉特征（例如，对象类别（例如，汽车和人））搜索视频内容的有效方法。但是，由于依赖于脱机处理，因此在记录视频流后无法立即搜索视频流及其捕获的功能。根据我们的调查，与YOLO v3对象检测器相比，缓存和存储实时视频流耗时更多。这种观察促使我们提出一种实时特征索引（RTFI）系统，以在通过对象检测器捕获和处理视频流后，对实时视频流进行基于即时特征的索引。 RTFI通过整合元数据结构和数据放置的新颖设计，现代对象检测器（即YOLO v3）的功能以及避免重复存储视频内容的重复数据删除技术来实现其实时目标。值得注意的是，RTFI是第一个用于在实时视频流上实现基于特征的实时索引的系统设计。与没有建议设计的基本系统相比，RTFI在Linux服务器上实现，可以将系统吞吐量提高多达10.60倍。此外，RTFI能够使所提出的系统在接收到视频内容后的20毫秒内，搜索10个实时视频流中的视频内容，而无需考虑网络传输延迟。

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