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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.
An Abnormal Activity Monitoring System Using Sensors and Video
Sang-soo Kim, Sun-woo Kim, Yeon-sung Choi
In this paper, we presents a system to ensure the safety of residents through appropriate action or alarm in case the residents occurs an emergency situation and abnormal activity. We collect and analysis real-time data of living environment of the residents using video and sensor. The existing system have been determined by using only the sensor data it have several problems. Our system attach camera to solve the existing system problem. We use weighted difference image and motion vector. The existing system, it takes about 48 hours to determine that an abnormal activity occurs. However, our system takes less than 1 hour.
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