Action Recognition using Aerial Videos for Surveillance Scenarios

Abstract

Surveillance using aerial videos has emerged as a critical area of study with diverse applications in domains such as military operations, law enforcement activities, traffic monitoring, and disaster management. Accurate detection and recognition of human actions play a pivotal role in aerial surveillance, enabling the identification of potential threats and suspicious behavior. This thesis introduces a novel framework for action recognition in aerial videos, employing the YOLO-Pose algorithm and LSTM network. The proposed framework consists of two primary stages. Firstly, the YOLO-Pose algorithm is utilized to extract 17 key points, representing the body pose, from each video frame. Secondly, an LSTM network is employed to classify human actions. To evaluate the framework, the Drone Action Dataset, comprising 13 distinct human action classes, is employed. The dataset is divided into three subsets for training and testing purposes. To capture the temporal dynamics of human actions, the extracted key points are normalized and segmented into 30 frame chunks. Subsequently, the LSTM network processes these chunks as input, generating a probability distribution over the 13 action classes. The evaluation of the proposed framework on the Drone Action Dataset demonstrates its effectiveness, achieving a notable accuracy of 80%. Furthermore, the experimental results establish that the proposed framework surpasses existing state-of-the-art methods for action recognition in aerial videos. Hence, the proposed framework presents a robust and efficient solution for action recognition in aerial videos, particularly within surveillance scenarios. By combining the YOLO-Pose algorithm and LSTM network, it effectively captures both spatial and temporal information of human actions, resulting in enhanced accuracy. The framework holds promising potential for application across various domains, including military operations, law enforcement, and disaster management.