DOI: 10.57760/sciencedb.20543
title: CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis
title: CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis

description: The CHIRLA dataset (Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis) is designed for long-term person re-identification (Re-ID) in real-world scenarios. The dataset consists of multi-camera video recordings captured over seven months in an indoor office environment. This dataset aims to facilitate the development and evaluation of Re-ID algorithms capable of handling significant variations in individuals’ appearances, including changes in clothing and physical characteristics. The dataset includes 22 individuals with 963,554 bounding box annotations across 596,345 frames.For more details refers to CHIRLA paper: https://arxiv.org/pdf/2502.06681Data Generation ProceduresThe dataset was recorded at the Robotics, Vision, and Intelligent Systems Research Group headquarters at the University of Alicante, Spain. Seven strategically placed Reolink RLC-410W cameras were used to capture videos in a typical office setting, covering areas such as laboratories, hallways, and shared workspaces. Each camera features a 1/2.7" CMOS image sensor with a 5.0-megapixel resolution and an 80° horizontal field of view. The cameras were connected via Ethernet and WiFi to ensure stable streaming and synchronization.A ROS-based interconnection framework was used to synchronize and retrieve images from all cameras. The dataset includes video recordings at a resolution of 1080×720 pixels, with a consistent frame rate of 30 fps, stored in AVI format with DivX MPEG-4 encoding.Data Processing Methods and StepsData processing involved a semi-automatic labeling procedure:Detection: YOLOv8x was used to detect individuals in video frames and extract bounding boxes.Tracking: The Deep SORT algorithm was employed to generate tracklets and assign unique IDs to detected individuals.Manual Verification: A custom graphical user interface (GUI) was developed to facilitate manual verification and correction of the automatically generated labels.Bounding boxes and IDs were assigned consistently across different cameras and sequences to maintain identity coherence.Data Structure and FormatThe dataset comprises:Video Files: 70 videos, each corresponding to a specific camera view in a sequence, stored in AVI format.Annotation Files: JSON files containing frame-wise annotations, including bounding box coordinates and identity labels.Benchmark Data: Processed image crops organized for ReID and tracking evaluationThe dataset is structured as follows:videos/seq_XXX/camera_Y.avi: Video files for each camera view.annotations/seq_XXX/camera_Y.json: Annotation files providing labeled bounding boxes and IDs.benchmark: Train and test data to use in two benchmarks proposed for tracking and Re-ID tasks in different scenarios.Datail data directory struture:CHIRLA_dataset/
├── videos/ # Raw video files
│ └── seq_XXX/
│ └── camera_Y.avi # Video files for each camera view
├── annotations/ # Frame-level annotations
│ └── seq_XXX/
│ └── camera_Y.json # Bounding boxes and IDs
└── benchmark/ # Processed benchmark data
 ├── reid/ # Person Re-Identification
 │ ├── long_term/ # Long-term ReID scenario
 │ │ ├── train/
 │ │ │ ├── train_0/
 │ │ │ │ └── seq_XXX/
 │ │ │ └── train_1/
 │ │ └── test/
 │ │ ├── test_0/ # Validation subset
 │ │ └── test_1/ # Test subset
 │ ├── multi_camera/ # Multi-camera ReID
 │ ├── multi_camera_long_term/ # Combined scenario
 │ └── reappearance/ # Reappearance detection
 └── tracking/ # Person Tracking
 ├── brief_occlusions/ # Short-term occlusions
 └── multiple_people_occlusions/ # Multi-person scenarios
For more information on how to use the benchmark data refers to CHIRLA github repository: https://github.com/bdager/CHIRLA and paper: https://arxiv.org/pdf/2502.06681 .Use Cases and ReusabilityThe CHIRLA dataset is suitable for:Long-term person re-identificationMulti-camera tracking and re-identificationSingle-camera tracking and re-identificationCitationIf you use CHIRLA dataset and benchmark, please cite the work as:@article{bdager2025chirla,title={CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis},author={Dominguez-Dager, Bessie and Escalona, Felix and Gomez-Donoso, Fran and Cazorla, Miguel},journal={arXiv preprint arXiv:2502.06681},year={2025},}
description: The CHIRLA dataset (Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis) is designed for long-term person re-identification (Re-ID) in real-world scenarios. The dataset consists of multi-camera video recordings captured over seven months in an indoor office environment. This dataset aims to facilitate the development and evaluation of Re-ID algorithms capable of handling significant variations in individuals’ appearances, including changes in clothing and physical characteristics. The dataset includes 22 individuals with 963,554 bounding box annotations across 596,345 frames.For more details refers to CHIRLA paper: https://arxiv.org/pdf/2502.06681Data Generation ProceduresThe dataset was recorded at the Robotics, Vision, and Intelligent Systems Research Group headquarters at the University of Alicante, Spain. Seven strategically placed Reolink RLC-410W cameras were used to capture videos in a typical office setting, covering areas such as laboratories, hallways, and shared workspaces. Each camera features a 1/2.7" CMOS image sensor with a 5.0-megapixel resolution and an 80° horizontal field of view. The cameras were connected via Ethernet and WiFi to ensure stable streaming and synchronization.A ROS-based interconnection framework was used to synchronize and retrieve images from all cameras. The dataset includes video recordings at a resolution of 1080×720 pixels, with a consistent frame rate of 30 fps, stored in AVI format with DivX MPEG-4 encoding.Data Processing Methods and StepsData processing involved a semi-automatic labeling procedure:Detection: YOLOv8x was used to detect individuals in video frames and extract bounding boxes.Tracking: The Deep SORT algorithm was employed to generate tracklets and assign unique IDs to detected individuals.Manual Verification: A custom graphical user interface (GUI) was developed to facilitate manual verification and correction of the automatically generated labels.Bounding boxes and IDs were assigned consistently across different cameras and sequences to maintain identity coherence.Data Structure and FormatThe dataset comprises:Video Files: 70 videos, each corresponding to a specific camera view in a sequence, stored in AVI format.Annotation Files: JSON files containing frame-wise annotations, including bounding box coordinates and identity labels.Benchmark Data: Processed image crops organized for ReID and tracking evaluationThe dataset is structured as follows:videos/seq_XXX/camera_Y.avi: Video files for each camera view.annotations/seq_XXX/camera_Y.json: Annotation files providing labeled bounding boxes and IDs.benchmark: Train and test data to use in two benchmarks proposed for tracking and Re-ID tasks in different scenarios.Datail data directory struture:CHIRLA_dataset/
├── videos/ # Raw video files
│ └── seq_XXX/
│ └── camera_Y.avi # Video files for each camera view
├── annotations/ # Frame-level annotations
│ └── seq_XXX/
│ └── camera_Y.json # Bounding boxes and IDs
└── benchmark/ # Processed benchmark data
 ├── reid/ # Person Re-Identification
 │ ├── long_term/ # Long-term ReID scenario
 │ │ ├── train/
 │ │ │ ├── train_0/
 │ │ │ │ └── seq_XXX/
 │ │ │ └── train_1/
 │ │ └── test/
 │ │ ├── test_0/ # Validation subset
 │ │ └── test_1/ # Test subset
 │ ├── multi_camera/ # Multi-camera ReID
 │ ├── multi_camera_long_term/ # Combined scenario
 │ └── reappearance/ # Reappearance detection
 └── tracking/ # Person Tracking
 ├── brief_occlusions/ # Short-term occlusions
 └── multiple_people_occlusions/ # Multi-person scenarios
For more information on how to use the benchmark data refers to CHIRLA github repository: https://github.com/bdager/CHIRLA and paper: https://arxiv.org/pdf/2502.06681 .Use Cases and ReusabilityThe CHIRLA dataset is suitable for:Long-term person re-identificationMulti-camera tracking and re-identificationSingle-camera tracking and re-identificationCitationIf you use CHIRLA dataset and benchmark, please cite the work as:@article{bdager2025chirla,title={CHIRLA: Comprehensive High-resolution Identification and Re-identification for Large-scale Analysis},author={Dominguez-Dager, Bessie and Escalona, Felix and Gomez-Donoso, Fran and Cazorla, Miguel},journal={arXiv preprint arXiv:2502.06681},year={2025},}