An Adaptive Abnormal Behavior Detection using Online Sequential Learning

Ito, Rei; Tsukada, Mineto; Masaaki, Kondo; Matsutani, Hiroki

doi:10.1109/cse/euc.2019.00087

Cited by 14 publications

(6 citation statements)

References 8 publications

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“…In their study, the backbone features of the target were extracted to remove other features to improve target accuracy. Ito et al used the OS-ELM and an autoencoder for adaptive abnormal behavior detection [ 34 ]. This method has high accuracy in detecting known abnormal behaviors; however, unknown anomalies cannot be detected.…”

Section: Related Workmentioning

confidence: 99%

CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior

Cai

Song

Xiong

et al. 2022

Sensors

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The widespread use of unmanned aerial vehicles (UAVs) has brought many benefits, particularly for military and civil applications. For example, UAVs can be used in communication, ecological surveys, agriculture, and logistics to improve efficiency and reduce the required workforce. However, the malicious use of UAVs can significantly endanger public safety and pose many challenges to society. Therefore, detecting malicious UAVs is an important and urgent issue that needs to be addressed. In this study, a combined UAV detection model (CUDM) based on analyzing video abnormal behavior is proposed. CUDM uses abnormal behavior detection models to improve the traditional object detection process. The work of CUDM can be divided into two stages. In the first stage, our model cuts the video into images and uses the abnormal behavior detection model to remove a large number of useless images, improving the efficiency and real-time detection of suspicious targets. In the second stage, CUDM works to identify whether the suspicious target is a UAV or not. Besides, CUDM relies only on ordinary equipment such as surveillance cameras, avoiding the use of expensive equipment such as radars. A self-made UAV dataset was constructed to verify the reliability of CUDM. The results show that CUDM not only maintains the same accuracy as state-of-the-art object detection models but also reduces the workload by 32%. Moreover, it can detect malicious UAVs in real-time.

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Section: Related Workmentioning

confidence: 99%

CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior

Cai

Song

Xiong

et al. 2022

Sensors

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“…In practice, anomaly patterns should be accurately detected from multiple normal patterns. To improve the accuracy of anomaly detection in such cases, we employ multiple ondevice learning instances, each of which is specialized for each normal pattern as proposed in [17]. Also, the number of the on-device learning instances can be dynamically tuned at runtime as proposed in [17].…”

Section: On-device Federated Learningmentioning

confidence: 99%

An On-Device Federated Learning Approach for Cooperative Model Update Between Edge Devices

2021

Self Cite

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Most edge AI focuses on prediction tasks on resource-limited edge devices while the training is done at server machines. However, retraining or customizing a model is required at edge devices as the model is becoming outdated due to environmental changes over time. To follow such a concept drift, a neural-network based on-device learning approach is recently proposed, so that edge devices train incoming data at runtime to update their model. In this case, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device. To address this issue, one approach is a cooperative learning or federated learning, where edge devices exchange their trained results and update their model by using those collected from the other devices. In this paper, as an on-device learning algorithm, we focus on OS-ELM (Online Sequential Extreme Learning Machine) to sequentially train a model based on recent samples and combine it with autoencoder for anomaly detection. We extend it for an on-device federated learning so that edge devices can exchange their trained results and update their model by using those collected from the other edge devices. This cooperative model update is oneshot while it can be repeatedly applied to synchronize their model. Our approach is evaluated with anomaly detection tasks generated from a driving dataset of cars, a human activity dataset, and MNIST dataset. The results demonstrate that the proposed on-device federated learning can produce a merged model by integrating trained results from multiple edge devices as accurately as traditional backpropagation based neural networks and a traditional federated learning approach with lower computation or communication cost.

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“…Abnormal behavior is a mechanism for detecting unusual behavior or state transitions of a targeted object. Any behavior that is outside normal behavior is called abnormal behavior [1]. To detect abnormal behavior, many sensors are used, such as video from static cameras [2]- [6], drones [7]- [9], and thermal cameras [10]- [13].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Methods for Human Abnormal Behavior Detection in Surveillance Systems; A Survey

Wastupranata

2024

Preprint

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Human Abnormal Behavior Detection is needed in several important sectors, especially in the field of public safety. Video Surveillance Systems are a trigger in detecting strange human behavior. However, this abnormal behavior data is rare and requires more costs to obtain, so the unsupervised learning method is used to study normal human behavior patterns only. There have been several previous studies that have provided significant results using this technique. There have been several previous survey studies related to human abnormal behavior detection, but they did not focus on unsupervised learning methods and the scope was too broad. In this paper, I present a survey regarding human abnormal behavior detection in more depth conceptually and classify it into two large parts, such as reconstruction-based detection and generative-based detection. This paper also offers a more extensive comparison with several popular datasets that are often used in previous research. Finally, I also shared several challenges and open research issues that emerged from our survey regarding future directions for future development.

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An Adaptive Abnormal Behavior Detection using Online Sequential Learning

Cited by 14 publications

References 8 publications

CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior

CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior

An On-Device Federated Learning Approach for Cooperative Model Update Between Edge Devices

Unsupervised Learning Methods for Human Abnormal Behavior Detection in Surveillance Systems; A Survey

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