Real-time Detection of Specific Events: A Case Study of Detecting Falls

Moon, Sangook; Yang, Ching-Hu; Kang, Beoungwoo; Kang, Suk‐Ju

doi:10.5573/ieiespc.2023.12.2.171

Cited by 1 publication

(5 citation statements)

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“…First, there are three studies on pedestrian anomaly analysis methods that can be used on edge devices, i.e., refs. [11][12][13], both of which use the object detection results of the YOLOv7 [13] model and propose a method for the analysis of pedestrian anomalies in the rule base. Reference [11] proposes a real-time assault event detection method based on object tracking to detect assaults in CCTV images.…”

Section: Pedestrian Abnormal Behavior Detection In Surveillance Videosmentioning

confidence: 99%

“…Reference [11] proposes a real-time assault event detection method based on object tracking to detect assaults in CCTV images. Reference [12] focuses on the real-time detection of fall events. These two studies propose efficient algorithms and system structures for the real-time processing of CCTV videos based on object detection.…”

Section: Pedestrian Abnormal Behavior Detection In Surveillance Videosmentioning

confidence: 99%

“…The YOLOv7-tiny model is trained on CCTV video datasets based on the learning method in [18,19] and optimized with FP16 lightweight and TensorRT [20] conversion. Assault and fall event detection for pedestrian safety leverages the assault analysis engine from [11] and the fall analysis engine from [12], respectively. Both pedestrian event analysis engines are rule-based and can be processed in real time.…”

Section: Experimental Environmentmentioning

confidence: 99%

“…Both pedestrian event analysis engines are rule-based and can be processed in real time. To meet the requirements of different object tracking models for pedestrian anomaly analysis, the ByteTrack [21] model is used in [11] and the SORT [22] model is used in [12]. The development environment for the edge device includes JetPack 4.5, Python 3.6, DeepStream SDK 5.1 [23], and TensorRT 8.4, utilizing the YOLOv7 TensorRT model.…”

Section: Experimental Environmentmentioning

confidence: 99%

“…In this paper, we evaluate the datasets used in the studies [11,12] to validate the proposed system by creating a combined video, as shown in Figure 7, and labeling the pedestrian misbehavior events and their onset and end times in the label file. Each combined movie contains ten clips of assault and fall events.…”

Section: Datasetsmentioning

confidence: 99%

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Pedestrian Abnormal Behavior Detection System Using Edge–Server Architecture for Large–Scale CCTV Environments

Song,

Nang

2024

Applied Sciences

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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.

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Section: Pedestrian Abnormal Behavior Detection In Surveillance Videosmentioning

confidence: 99%

Section: Pedestrian Abnormal Behavior Detection In Surveillance Videosmentioning

confidence: 99%

Section: Experimental Environmentmentioning

confidence: 99%