Empowering Agentic Video Analytics Systems with Video Language Models

• URL: http://arxiv.org/abs/2505.00254v1
• Date: Thu, 01 May 2025 02:40:23 GMT
• Title: Empowering Agentic Video Analytics Systems with Video Language Models
• Authors: Yuxuan Yan, Shiqi Jiang, Ting Cao, Yifan Yang, Qianqian Yang, Yuanchao Shu, Yuqing Yang, Lili Qiu,
• Abstract summary: Video-Language Models (VLMs) offer potential for enabling open-ended video understanding, reasoning, and analytics.<n>VLMs' limited context windows present challenges when processing ultra-long video content.<n>We introduce AVA, a VLM-powered system designed for open-ended, advanced video analytics.
• Score: 16.962065998310223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-driven video analytics has become increasingly pivotal across diverse domains. However, existing systems are often constrained to specific, predefined tasks, limiting their adaptability in open-ended analytical scenarios. The recent emergence of Video-Language Models (VLMs) as transformative technologies offers significant potential for enabling open-ended video understanding, reasoning, and analytics. Nevertheless, their limited context windows present challenges when processing ultra-long video content, which is prevalent in real-world applications. To address this, we introduce AVA, a VLM-powered system designed for open-ended, advanced video analytics. AVA incorporates two key innovations: (1) the near real-time construction of Event Knowledge Graphs (EKGs) for efficient indexing of long or continuous video streams, and (2) an agentic retrieval-generation mechanism that leverages EKGs to handle complex and diverse queries. Comprehensive evaluations on public benchmarks, LVBench and VideoMME-Long, demonstrate that AVA achieves state-of-the-art performance, attaining 62.3% and 64.1% accuracy, respectively, significantly surpassing existing VLM and video Retrieval-Augmented Generation (RAG) systems. Furthermore, to evaluate video analytics in ultra-long and open-world video scenarios, we introduce a new benchmark, AVA-100. This benchmark comprises 8 videos, each exceeding 10 hours in duration, along with 120 manually annotated, diverse, and complex question-answer pairs. On AVA-100, AVA achieves top-tier performance with an accuracy of 75.8%.

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