As the deployment of CCTV cameras for safety continues to increase, the monitoring workload has significantly exceeded the capacity of the current workforce. To overcome this problem, intelligent CCTV technologies and server-efficient deep learning analysis models are being developed. However, real-world applications exhibit performance degradation due to environmental changes and limited server processing capacity for multiple CCTVs. This study proposes a real-time pedestrian anomaly detection system with an edge–server structure that ensures efficiency and scalability. In the proposed system, the pedestrian abnormal behavior detection model analyzed by the edge uses a rule-based mechanism that can detect anomalies frequently, albeit less accurately, with high recall. The server uses a deep learning-based model with high precision because it analyzes only the sections detected by the edge. The proposed system was applied to an experimental environment using 20 video streams, 18 edge devices, and 3 servers equipped with 2 GPUs as a substitute for real CCTV. Pedestrian abnormal behavior was included in each video stream to conduct experiments in real-time processing and compare the abnormal behavior detection performance between the case with the edge and server alone and that with the edge and server in combination. Through these experiments, we verified that 20 video streams can be processed with 18 edges and 3 GPU servers, which confirms the scalability of the proposed system according to the number of events per hour and the event duration. We also demonstrate that the pedestrian anomaly detection model with the edge and server is more efficient and scalable than the models with these components alone. The linkage of the edge and server can reduce the false detection rate and provide a more accurate analysis. This research contributes to the development of control systems in urban safety and public security by proposing an efficient and scalable analysis system for large-scale CCTV environments.

The proliferation of media-sharing platforms has led to issues with illegally edited content and the distribution of pornography. To protect personal information, de-identification technologies are being developed to prevent facial identification. Existing de-identification methods directly alter the pixel values in the face region, leading to reduced feature representation and identification accuracy. This study aims to develop a method that minimizes the possibility of personal identification while effectively preserving important features for image- and video-copy-detection tasks, proposing a new deep-learning-based de-identification approach that surpasses traditional pixel-based alteration methods. We introduce two de-identification models using different approaches: one emphasizing the contours of the original face through feature inversion and the other generating a blurred version of the face using D2GAN (Dual Discriminator Generative Adversarial Network). Both models were evaluated on their performance in image- and video-copy-detection tasks before and after de-identification, demonstrating effective feature preservation. This research presents new possibilities for personal-information protection and digital-content security, contributing to digital-rights management and law enforcement.

Non-manual signals, such as facial expressions and eye gaze, play a crucial role in sign language communication, comparable to manual signals. However, previous studies in sign language processing have primarily focused on manual signals and often treat non-manual signals only as auxiliary cues for improving recognition or translation performance. Furthermore, most existing sign language datasets lack explicit annotations for non-manual signals due to the high cost of manual labeling, which limits systematic analysis and independent modeling of these signals. This limitation remains an ongoing challenge in sign language research. To address this gap, we conduct a systematic study on non-manual signal classification using the Korean–Korean Sign Language Parallel Corpus 2022 (Version 1.0), one of the few publicly available datasets that provides structured annotations for non-manual signals. However, the dataset suffers from severe class imbalance among annotated non-manual signal labels, making effective learning difficult—particularly for underrepresented classes. To address this problem, we propose an improved framework that systematically analyzes and classifies non-manual signals using long-tail learning techniques, including loss reweighting, resampling, and focal-based optimization. Experimental results show that the proposed approach improves the Macro-F1 score from 0.6065 to 0.7610 compared with the baseline model, achieving substantial gains for minority classes. We further demonstrate that the model can estimate the temporal occurrence of non-manual signals without explicit frame-level annotations, enabling soft-label generation for unlabeled data. These findings provide a practical direction for reducing annotation costs and supporting scalable analysis of non-manual signals in sign language understanding.