• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3769-3789
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• 2018

Social pedestrian groups are the basic elements that constitute a crowd; therefore, detection of such groups is scientifically important for modeling social behavior, as well as practically useful for crowd video understanding. A social group refers to a cluster of members who tend to keep similar motion state for a sustained period of time. One of the main challenges of social group detection arises from the complex dynamic variations of crowd patterns. Therefore, most works model dynamic groups to analysis the crowd behavior, ignoring the existence of stationary groups in crowd scene. However, in this paper, we propose a novel unified framework for detecting social pedestrian groups in crowd videos, including dynamic and stationary pedestrian groups, based on spatiotemporal-oriented energy measurements. Dynamic pedestrian groups are hierarchically clustered based on energy flow similarities and trajectory motion correlations between the atomic groups extracted from principal spatiotemporal-oriented energies. Furthermore, the probability distribution of static spatiotemporal-oriented energies is modeled to detect stationary pedestrian groups. Extensive experiments on challenging datasets demonstrate that our method can achieve superior results for social pedestrian group detection and crowd video classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.912-928
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• 2019

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.

• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.49-60
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• 2023

After the 10.29 Itaewon disaster, interests in the crowd crush injury increased, but it is pointed out that the academic and practical basis related to crowd crush is still weak in Korea. Therefore, in this study, terms and concepts related to crowd crush were investigated and proposed, and representative cases of crowd crush events were investigated and summarized. Approaches based on representative cases were investigated, and among them, video analysis, simulation, questionnaire survey and interview methods were derived as an essential approach methods. Through this research, it is expected that standardization of Korean terminology, concept establishment, evaluation, and systematization of approach methods of crowd crush can be accomplished.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5530-5546
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• 2016

Automatic analysis and understanding of human activities is a challenging task in computer vision, especially for the surveillance scenarios which typically contains crowds, complex motions and occlusions. To address these issues, a Bag-of-words representation of videos is developed by leveraging information including crowd positions, motion directions and velocities. We infer the crowd activity in a motion field using Category Constrained Correlated Topic Model (CC-CTM) with latent topics. We represent each video by a mixture of learned motion patterns, and predict the associated activity by training a SVM classifier. The experiment dataset we constructed are from Crowd_PETS09 bench dataset and UCF_Crowds dataset, including 2000 documents. Experimental results demonstrate that accuracy reaches 90%, and the proposed approach outperforms the state-of-the-arts by a large margin.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.7-14
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• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• International Journal of Contents
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• v.16 no.4
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• pp.84-97
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• 2020

Since national image video advertisements are a means of public relations with wide delivery and major repercussions at home and abroad, this study performs a comparative analysis of video advertisements aired by previous governments as reported by Daejung Kim, Moohyun Roh, Myungbak Lee, and Geunhye Park. The characteristics of the previous governments, whether or not social trends were reflected, and the importance of traditional and modern elements were examined. As we describe, there are clear differences in video advertisements by government regime, and while messages expressing traditional values of Eastern culture have gradually decreased, reflecting the trends of the times, the messages emphasizing the modern values of the West have gradually increased. Our research confirmed that traditional elements such as 'Samulnori' and 'Taekwondo' are gradually disappearing. In addition, it was confirmed that the collective values, traditional values, and humanism represented by the 'large crowd' and 'traditional elements' in the Kim Dae-jung and Roh Moo-hyun administration changed in the Lee Myung-bak and Park Geun-hye administrations to more individualistic, materialistic, Western values. This study is meaningful in that it analyzed the components and characteristics of national image advertisements by governments in the past, how social trends were reflected, and the weight difference between traditional and modern elements. Based on this research, the significance of the current Moon Jae-in government's groundwork for follow-up research should not be understated.

• Fire Science and Engineering
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• v.31 no.1
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• pp.36-41
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• 2017

In this study, the experimental measurements from a one-way moving experiment showed that the average movement speed was 0.55 m/s with an average crowd density of $2.36P/m^2$ in a corridor. The cCalculation result of the correlations between the crowd density and movement speed from the SFPE Handbook showed an average of 0.53 m/s. The difference between the calculation and experiment was 0.02 m/s. A comparison of each data set showed that the maximum difference was 0.38 m/s. Some experimental results showed that the crowd density increased with increasing movement speed and the average data from the entire experiment time was used for the analysis. When the short time interval for frame by frame analysis for video files was conducted, the experimental data was expected to be more reliable.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.