Dataset of Unconstrained Large Gathering Images for Person Identification and Tracking

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed in the premises of Al Nabvi mosque, Madinah, Saudi Arabia. This dataset consists of both raw and processed images reflecting a highly challenging and unconstraint environment. The methodology for the development of the dataset consists of four core phases, 1) Acquisition of videos, 2) Extraction of frames, 3) Localization of face regions, and 4) Cropping and resizing of detected face regions. The raw images in the dataset consist of a total of 4613 frames obtained from video sequences. The processed images in the dataset consist of the face regions of 250 persons which were extracted from raw data images to ensure the authenticity of the presented data. The dataset further consists of 8 images corresponding to each of the 250 subjects (persons) for a total of 2000 images. The dataset portrays a highly unconstrained and challenging environment, where human faces of varying sizes and pixel quality (resolution) can be observed. Since the face regions in video sequences are severely degraded due to various unavoidable factors, it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes. We have also gathered and displayed records of the presence of subjects who appear in presented frames; in a temporal context. This can also be used as a temporal benchmark for tracking, finding persons, activity monitoring, and crowd counting in large crowd scenarios