Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

IndoorCrowd: A Multi-Scene Dataset for Human Detection, Segmentation, and Tracking with an Automated Annotation Pipeline

Sebastian-Ion Nae, Radu Moldoveanu, Alexandra Stefania Ghita, Adina Magda Florea

Abstract

Understanding human behaviour in crowded indoor environments is central to surveillance, smart buildings, and human-robot interaction, yet existing datasets rarely capture real-world indoor complexity at scale. We introduce IndoorCrowd, a multi-scene dataset for indoor human detection, instance segmentation, and multi-object tracking, collected across four campus locations (ACS-EC, ACS-EG, IE-Central, R-Central). It comprises $31$ videos ($9{,}913$ frames at $5$fps) with human-verified, per-instance segmentation masks. A $620$-frame control subset benchmarks three foundation-model auto-annotators: SAM3, GroundingSAM, and EfficientGroundingSAM, against human labels using Cohen's $κ$, AP, precision, recall, and mask IoU. A further $2{,}552$-frame subset supports multi-object tracking with continuous identity tracks in MOTChallenge format. We establish detection, segmentation, and tracking baselines using YOLOv8n, YOLOv26n, and RT-DETR-L paired with ByteTrack, BoT-SORT, and OC-SORT. Per-scene analysis reveals substantial difficulty variation driven by crowd density, scale, and occlusion: ACS-EC, with $79.3\%$ dense frames and a mean instance scale of $60.8$px, is the most challenging scene. The project page is available at https://sheepseb.github.io/IndoorCrowd/.

IndoorCrowd: A Multi-Scene Dataset for Human Detection, Segmentation, and Tracking with an Automated Annotation Pipeline

Abstract

Understanding human behaviour in crowded indoor environments is central to surveillance, smart buildings, and human-robot interaction, yet existing datasets rarely capture real-world indoor complexity at scale. We introduce IndoorCrowd, a multi-scene dataset for indoor human detection, instance segmentation, and multi-object tracking, collected across four campus locations (ACS-EC, ACS-EG, IE-Central, R-Central). It comprises videos ( frames at fps) with human-verified, per-instance segmentation masks. A -frame control subset benchmarks three foundation-model auto-annotators: SAM3, GroundingSAM, and EfficientGroundingSAM, against human labels using Cohen's , AP, precision, recall, and mask IoU. A further -frame subset supports multi-object tracking with continuous identity tracks in MOTChallenge format. We establish detection, segmentation, and tracking baselines using YOLOv8n, YOLOv26n, and RT-DETR-L paired with ByteTrack, BoT-SORT, and OC-SORT. Per-scene analysis reveals substantial difficulty variation driven by crowd density, scale, and occlusion: ACS-EC, with dense frames and a mean instance scale of px, is the most challenging scene. The project page is available at https://sheepseb.github.io/IndoorCrowd/.

Paper Structure

This paper contains 18 sections, 3 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Collection
  4. Recording Setup
  5. Scenes
  6. Frame Sampling and Video Splits
  7. Annotation Pipeline
  8. Ethics and Privacy
  9. Dataset Statistics and Analysis
  10. Crowd Density and Scene Diversity
  11. Scale and Aspect Ratio Variation
  12. Occlusion Analysis
  13. Auto-Labelling Quality
  14. MOT Subset
  15. Benchmarks
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Representative frames from the four dataset scenes (left to right): ACS-EC ground-level view showing dense seating and circulation areas; ACS-EC elevated view providing a top-down perspective of the same atrium; ACS-EG narrow corridor with strong near-to-distal scale variance; IE-Central entrance hall captured from an elevated angle; and R-Central central atrium with prominent structural columns and overhead viewpoint.
  • Figure 2: Spatial density heatmaps showing the normalised distribution of person bounding-box centres across all annotated frames per scene. Colour encodes relative density (yellow $\to$ dark red = low $\to$ high). The heatmaps reveal four distinct spatial regimes: a dominant horizontal band in ACS-EC driven by circulation traffic and stationary occupants in the common area; a concentrated mid-depth cluster in ACS-EG reflecting strong scale variance along its linear corridor axis; three discrete zones in IE-Central spanning the entry, corridor junction, and seating area; and a diffuse, column-interrupted spread in R-Central where the overhead viewpoint collapses ascending and descending pedestrian flow into a single projection. These spatial patterns directly inform the per-scene variation in crowd density, occlusion rate, and detection difficulty reported in Sections \ref{['subsec:occlusion']} and \ref{['subsec:autolabel_quality']}.
  • Figure 3: Qualitative comparison of auto-labelling methods across ACS-EC, IE-Central, and R-Central (rows, top to bottom). Columns show the raw image, SAM3, GroundingSAM, and human ground truth, with per-frame instance counts (n). SAM3 produces false positives on ACS-EC (row 1); GroundingSAM misses occluded persons across all scenes.