Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Case Study in Responsible AI-Assisted Video Solutions: Multi-Metric Behavioral Insights in a Public Market Setting

Mehrnoush Fereydouni, Eka Ebong, Sahar Maleki, Philip Otienoburu, Babak Rahimi Ardabili, Hamed Tabkhi

TL;DR

The results demonstrate that AI-enabled video solutions can be successfully integrated into urban environments to provide high-fidelity spatial analytics while maintaining strict adherence to privacy and social responsibility.

Abstract

Despite recent advances in Computer Vision and Artificial Intelligence (AI), AI-assisted video solutions have struggled to penetrate real-world urban environments due to significant concerns regarding privacy, ethical risks, and technical challenges like bias and explainability. This work addresses these barriers through a case study in a city-center public market, demonstrating a pathway for the responsible deployment of AI in community spaces. By adopting a user-centric methodology that prioritizes public trust and privacy safeguards, we show that detailed, operationally relevant behavioral insights can be derived from abstract data representations without compromising ethical standards. The study focuses on generating Multi-Metric Behavioral Insights through the extraction of three complementary signals: customer directional flow, dwell duration, and movement patterns. Utilizing human pose detection and complex behavioral analysis - processed through geometric normalization and motion modeling - the system remains robust under tracking fragmentation and occlusion. Data collected over 18 days, spanning routine operations and a festival window from May 2-4, reveals a consistently right-skewed dwell-time behavior. While most visits last approximately 3-4 minutes, peak activity periods increase the mean to roughly 22 minutes. Furthermore, movement analysis indicates uneven circulation, with over 60% of traffic concentrated in approximately 30% of the venue space. By mapping popular thoroughfares and high-traffic storefronts, this case study provides venue managers and business owners with objective, measurable information to optimize foot traffic. Ultimately, these results demonstrate that AI-enabled video solutions can be successfully integrated into urban environments to provide high-fidelity spatial analytics while maintaining strict adherence to privacy and social responsibility.

A Case Study in Responsible AI-Assisted Video Solutions: Multi-Metric Behavioral Insights in a Public Market Setting

TL;DR

The results demonstrate that AI-enabled video solutions can be successfully integrated into urban environments to provide high-fidelity spatial analytics while maintaining strict adherence to privacy and social responsibility.

Abstract

Despite recent advances in Computer Vision and Artificial Intelligence (AI), AI-assisted video solutions have struggled to penetrate real-world urban environments due to significant concerns regarding privacy, ethical risks, and technical challenges like bias and explainability. This work addresses these barriers through a case study in a city-center public market, demonstrating a pathway for the responsible deployment of AI in community spaces. By adopting a user-centric methodology that prioritizes public trust and privacy safeguards, we show that detailed, operationally relevant behavioral insights can be derived from abstract data representations without compromising ethical standards. The study focuses on generating Multi-Metric Behavioral Insights through the extraction of three complementary signals: customer directional flow, dwell duration, and movement patterns. Utilizing human pose detection and complex behavioral analysis - processed through geometric normalization and motion modeling - the system remains robust under tracking fragmentation and occlusion. Data collected over 18 days, spanning routine operations and a festival window from May 2-4, reveals a consistently right-skewed dwell-time behavior. While most visits last approximately 3-4 minutes, peak activity periods increase the mean to roughly 22 minutes. Furthermore, movement analysis indicates uneven circulation, with over 60% of traffic concentrated in approximately 30% of the venue space. By mapping popular thoroughfares and high-traffic storefronts, this case study provides venue managers and business owners with objective, measurable information to optimize foot traffic. Ultimately, these results demonstrate that AI-enabled video solutions can be successfully integrated into urban environments to provide high-fidelity spatial analytics while maintaining strict adherence to privacy and social responsibility.
Paper Structure (10 sections, 4 equations, 11 figures, 1 table)

This paper contains 10 sections, 4 equations, 11 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Setup and Data Collection
  4. Results
  5. Dwell Time
  6. Entry/exit counts
  7. Movement Patterns
  8. Zoning Movement Approach
  9. Coordinate Movement Approach
  10. Discussion

Figures (11)

  • Figure 1: End-to-end system architecture adapted from the Ancilia platform b1. All analytical components are performed at the statistical analysis stage using metadata extracted from the AI vision pipeline.
  • Figure 2: Seating ZOI for Camera 1 in pixel coordinates.
  • Figure 3: Rolling $(t{-}1)$ stability rule (15% threshold).
  • Figure 4: Distribution of dwell times within the Camera 1 ZOI on May 2, 2025.
  • Figure 5: Daily dwell-time distributions by day, with black markers representing the mean dwell time.
  • ...and 6 more figures