Students Attention Detection Dataset
Description
The purpose of the creation of this dataset for analyzing student's attention and behavior based on various visual cues. This dataset is generated using a combination of fundamental modules that enable the extraction of distinct high-level features from video frames. The key components involved in the dataset creation process are face detection, hand tracking, mobile phone detection, and pose estimation. These components collectively produce a set of features that characterize different aspects of student presence and orientation within the observed frames. The face detection module yields the count of detected faces, along with their coordinates, width, and height. The hand tracking module provides the count of identified hands. The pose estimation component generates head orientation, x-axis rotation, and y-axis rotation. The mobile phone detection module outputs six features: mobile phone presence, phone coordinates, width, height, and detection confidence. The dataset comprises a total of 17 columns, which consist of 16 feature columns and one label column. In total, there are 4,000 records within the dataset. The features are described as follows: no_of_face: Indicates the number of faces detected in a frame. face_x: Represents the x-coordinate of the upper-left corner of a detected face. face_y: Denotes the y-coordinate of the upper-left corner of a detected face. face_w: Reflects the width of a detected face in pixels. face_h: Specifies the height of a detected face in pixels. face_con: Provides the confidence score for face detection. no_of_hands: Indicates the number of hands detected in a frame. pose: Describes the orientation of a detected face (Forward, Left, Right, Down). pose_x: Represents the x-axis rotation of the detected face's orientation. pose_y: Represents the y-axis rotation of the detected face's orientation. phone: Indicates the presence of a mobile phone (0: No phone detected, 1: Phone detected). phone_x: Specifies the x-coordinate of the upper-left corner of a detected phone. phone_y: Denotes the y-coordinate of the upper-left corner of a detected phone. phone_w: Reflects the width of a detected phone in pixels. phone_h: Specifies the height of a detected phone in pixels. phone_con: Provides the confidence score for phone detection. label: Serves as the target column, indicating whether the subject is attentive (0) or inattentive (1). The features encompass a wide range of factors that contribute to assessing a student's level of attentiveness. The diverse set of features and corresponding label column of the dataset make it a valuable resource for training machine learning models aimed at recognizing and classifying attentive and inattentive behaviors of the student.