mlsanityData to 14 Oct 2026

Feedback Driven Multi Stereo Vision System for Real-Time Event Analysis

arXiv 2506.17910 ↗First posted 22 Jun 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

2D cameras are often used in interactive systems. Other systems like gaming consoles provide more powerful 3D cameras for short range depth sensing. Overall, these cameras are not reliable in large, complex environments. In this work, we propose a 3D stereo vision based pipeline for interactive systems, that is able to handle both ordinary and sensitive applications, through robust scene understanding. We explore the fusion of multiple 3D cameras to do full scene reconstruction, which allows for preforming a wide range of tasks, like event recognition, subject tracking, and notification. Using possible feedback approaches, the system can receive data from the subjects present in the environment, to learn to make better decisions, or to adapt to completely new environments. Throughout the paper, we introduce the pipeline and explain our preliminary experimentation and results. Finally, we draw the roadmap for the next steps that need to be taken, in order to get this pipeline into production

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 20 references that are in the map. First posted on arXiv 22 Jun 2025.

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7 of 20 references.

References · 7

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  3. 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 910
  4. 2016Simple Online and Realtime Trackingcited by 868
  5. 2016Fully-Convolutional Siamese Networks for Object Trackingcited by 568
  6. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  7. 2015Differential Recurrent Neural Networks for Action Recognitioncited by 39

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