Motioninsights: real-time object tracking in streaming video

IF 2.4 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Vision and Applications Pub Date : 2024-06-27 DOI:10.1007/s00138-024-01570-y

Dimitrios Banelas, Euripides G. M. Petrakis

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Abstract

MotionInsights facilitates object detection and tracking from multiple video streams in real-time. Leveraging the distributed stream processing capabilities of Apache Flink and Apache Kafka (as an intermediate message broker), the system models video processing as a data flow stream processing pipeline. Each video frame is split into smaller blocks, which are dispatched to be processed in parallel by a number of Flink operators. In the first stage, each block undergoes background subtraction and component labeling. The connected components from each frame are grouped, and the eligible components are merged into objects. In the last stage of the pipeline, all objects from each frame are concentrated to produce the trajectory of each object. The Flink application is deployed as a Kubernetes cluster in the Google Cloud Platform. Experimenting in a Flink cluster with 7 machines, revealed that MotionInsights achieves up to 6 times speedup compared to a monolithic (nonparallel) implementation while providing accurate trajectory patterns. The highest (i.e., more than 6 times) speed-up was observed with video streams of the highest resolution. Compared to existing systems that use custom or proprietary architectures, MotionInsights is independent of the underlying hardware platform and can be deployed on common CPU architectures and the cloud.

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Motioninsights：流媒体视频中的实时物体跟踪

MotionInsights 可实时从多个视频流中进行对象检测和跟踪。该系统利用 Apache Flink 和 Apache Kafka（作为中间消息代理）的分布式流处理功能，将视频处理模型化为数据流处理管道。每个视频帧都被分割成较小的块，由多个 Flink 操作员并行调度处理。在第一阶段，每个区块都要进行背景减除和组件标记。对每个帧中的连接组件进行分组，并将符合条件的组件合并为对象。在流水线的最后阶段，每个帧中的所有对象被集中起来，以生成每个对象的轨迹。Flink 应用程序作为 Kubernetes 集群部署在谷歌云平台上。在拥有 7 台机器的 Flink 集群中进行的实验表明，与单机（非并行）实现相比，MotionInsights 的速度最多可提高 6 倍，同时还能提供精确的轨迹模式。在最高分辨率的视频流中观察到了最高（即超过 6 倍）的速度提升。与使用定制或专有架构的现有系统相比，MotionInsights 与底层硬件平台无关，可以部署在普通 CPU 架构和云上。

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来源期刊

Machine Vision and Applications 工程技术-工程：电子与电气

CiteScore

6.30

自引率

3.00%

发文量

审稿时长

8.7 months

期刊介绍： Machine Vision and Applications publishes high-quality technical contributions in machine vision research and development. Specifically, the editors encourage submittals in all applications and engineering aspects of image-related computing. In particular, original contributions dealing with scientific, commercial, industrial, military, and biomedical applications of machine vision, are all within the scope of the journal. Particular emphasis is placed on engineering and technology aspects of image processing and computer vision. The following aspects of machine vision applications are of interest: algorithms, architectures, VLSI implementations, AI techniques and expert systems for machine vision, front-end sensing, multidimensional and multisensor machine vision, real-time techniques, image databases, virtual reality and visualization. Papers must include a significant experimental validation component.