A Large-scale Distributed Video Parsing and Evaluation Platform
Kai Yu, Yang Zhou, Da Li, Zhang Zhang, Kaiqi Huang

TL;DR
This paper presents a scalable, extensible platform for large-scale video parsing and evaluation in surveillance, integrating big data technologies and user feedback to improve recognition tasks.
Contribution
It introduces a novel distributed platform combining Spark Streaming, Kafka, and HDFS for scalable video analysis with a web interface for performance evaluation.
Findings
Supports large-scale surveillance video processing
Enables incremental algorithm development
Provides user feedback for system performance
Abstract
Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem (HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Advanced Image and Video Retrieval Techniques
