Infant Action Database: A Benchmark for Infant Action Recognition in Uncontrolled condition

Abstract

The main focus of our work is to create a database for action recognition of unattended infants in uncontrolled environment with wide variations in surroundings, lighting, interactions with objects, camera motions, etc., Action recognition of infants is emerging as an important and technically challenging computer vision problem as compared with adult action recognition because of their physical appearance. Most of the previous action recognition techniques have focused on the recognition of action captured under a controlled environment in a standard laboratory setting. In this study a novel database is introduced that can be used as a benchmark for surveillance parenting. This database involves nine normal and nine abnormal actions classes which consist of actions and movements of infants occurring in fairly uncontrolled conditions. This database consists of realistic user-uploaded videos which are recorded in the clustered background and different camera motion. After the collection of all videos, they are manually trimmed to form a database. To further evaluate the performance of the database, HOG features were extracted from database set and trained by different Machine Learning classifiers like Multi-class Naive Bayes, Support Vector Machine, Ensemble classifier, Discriminant analysis and Decision tree classifiers. This experimentation shows that the database is complex and robust that can serve as a base for testing action recognition algorithms.