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165 People Night Surveillance Person Re-Identification Dataset (RGB-IR, Attributes)
person re identification dataset
pedestrian reid dataset
reid dataset
surveillance dataset
cctv dataset
video surveillance dataset
This dataset comprises 165 individuals, captured in outdoor nighttime surveillance scenarios. It covers a diverse demographic—including both males and females and age groups ranging from children to middle-aged adults, with a predominance of young people—and features both RGB and infrared (IR) image modalities. Annotations include bounding boxes and 15 categories of human attributes, making the data suitable for person re-identification (ReID), multi-camera tracking, pedestrian detection, and surveillance video analysis tasks.
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Specifications
Data size
165 people, 32 images were annotated for each subject
Population distribution
149 brown people, 15 Asians, 1 Caucasian people
Gender distribution
87 males, 78 females
Age distribution
18 people under 18 years old, 135 people aged from 18 to 45 years old, 7 people aged from 46 to 50 years old
Collecting environment
outdoor scenes
Data diversity
different age groups, different time periods, different shooting angles, different human body orientations and postures, different modal cameras
Device
binocular surveillance cameras (IR+RGB)
Collecting angle
looking down angle
Collecting time
night
Data format
the image data format is .jpg, the annotation file format is .json
Annotation content
human body rectangular bounding boxes, 15 human body attributes; Label the subject’s gender, age, race, nationality, camera ID, camera height, camera modal
Acccuracy rate
a rectangular bounding box of human body is qualified when the deviation is not more than 5 pixels, and the qualified rate of the bounding boxes shall not be lower than 95% ; Annotation accuracy of human attributes is over 95%; The accuracy of label annotation is not less than 95%
What types of computer vision applications can Nexdata’s datasets support?
Nexdata’s computer vision datasets support a wide range of AI applications, including image classification, object detection, image segmentation, facial and human-related recognition, scene understanding, autonomous driving, and other visual perception tasks. Depending on the dataset, data may include images, videos, bounding boxes, polygons, keypoints, segmentation masks, text annotations, and other structured labels.
Can Nexdata customize Computer Vision datasets based on our specific requirements?
Yes. If our off-the-shelf Computer Vision datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, annotation, and curation services. We can customize data based on your target objects, environments, scenarios, camera specifications, geographic locations, data volume, annotation formats, and quality standards to support specific model training and evaluation needs.
How does Nexdata ensure the quality and scalability of its Computer Vision datasets?
Nexdata applies multi-stage quality control throughout data collection, annotation, validation, and delivery. Depending on project requirements, we can implement customized annotation guidelines, multi-level reviews, consistency checks, and quality sampling to ensure dataset accuracy and consistency. Our data collection and processing capabilities can also be scaled to support large-volume Computer Vision projects.
Specifications
Sample
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