Deep Learning-Based Abnormal Event Recognition Method for Detecting Pedestrian Abnormal Events in CCTV Video

Jinha Song, Youngjoon Hwang, Jongho Nang

http://doi.org/10.5626/JOK.2024.51.9.771

With increasing CCTV installations, the workload for monitoring has significantly increased. However, a growing workforce has reached its limits in addressing this issue. To overcome this problem, intelligent CCTV technology has been developed. However, this technology experiences performance degradation in various situations. This paper proposes a robust and versatile method for integrated abnormal behavior recognition in CCTV footage that could be applied in multiple situations. This method could extract frame images from videos to use raw images and heatmap representation images as inputs. It could remove feature vectors through merging methods at both image and feature vector levels. Based on these vectors, we proposed an abnormal behavior recognition method utilizing 2D CNN models, 3D CNN models, LSTM, and Average Pooling. We defined minor classes for performance validation and generated 1,957 abnormal behavior video clips for testing. The proposed method is expected to improve the accuracy of abnormal behavior recognition through CCTV footage, thereby enhancing the efficiency of security and surveillance systems.