Group Activity Recognition Based on Interaction Contextual Information in Videos Using Machine Learning

Abstract

This paper is about recognizing multiple person actions occurring in videos, including individual actions, interactions,and group activities. In an environment, multiple people perform group actions such as walking in groupsand talking by facing each other. The model develops by retrieving individual person action from video sequencesby representing interactive contextual features among multiple people. The novelty of the proposed frameworkis the development of interactive action context descriptors (IAC) and classifying group activities using MachineLearning. Each individual person and other nearby people’s relative action score are encoded by IAC in thevideo frame. Individual person action descriptors are important clues for recognition of multiple person activityby developing interaction context. An action retrieval technique was formulated based on KNN for individualaction classification scores. This model also introduces Fully Connected Conditional Random Field (FCCRF) tolearn interaction context information among multiple people. FCCRF regularizes activity categorization by thespatial-temporal model. This paper also presents threshold processing to improve the performance of contextdescriptors. The experimental results compared to state-of-the-art approaches and demonstrated improvement inperformance for group activity recognition.