VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streams

TL;DR

VidCEP is a novel in-memory framework that enables high-level, real-time detection of complex spatiotemporal patterns in video streams using a new query language and deep neural network-based event detection.

Contribution

The paper introduces VidCEP, a framework combining a graph-based video event representation, a new expressive query language, and a real-time event matcher for video streams.

Findings

Achieves F-score between 0.66 and 0.89 in pattern detection.

Maintains near real-time processing at 70 fps for five videos.

Sub-second latency in event matching.

Abstract

Video data is highly expressive and has traditionally been very difficult for a machine to interpret. Querying event patterns from video streams is challenging due to its unstructured representation. Middleware systems such as Complex Event Processing (CEP) mine patterns from data streams and send notifications to users in a timely fashion. Current CEP systems have inherent limitations to query video streams due to their unstructured data model and lack of expressive query language. In this work, we focus on a CEP framework where users can define high-level expressive queries over videos to detect a range of spatiotemporal event patterns. In this context, we propose: i) VidCEP, an in-memory, on the fly, near real-time complex event matching framework for video streams. The system uses a graph-based event representation for video streams which enables the detection of high-level semantic concepts from video using cascades of Deep Neural Network models, ii) a Video Event Query language (VEQL) to express high-level user queries for video streams in CEP, iii) a complex event matcher to detect spatiotemporal video event patterns by matching expressive user queries over video data. The proposed approach detects spatiotemporal video event patterns with an F-score ranging from 0.66 to 0.89. VidCEP maintains near real-time performance with an average throughput of 70 frames per second for 5 parallel videos with sub-second matching latency.