3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos

Dubey, Shikha; Boragule, Abhijeet; Jeon, Moongu

doi:10.1109/iccais46528.2019.9074586

Cited by 36 publications

(17 citation statements)

References 16 publications

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“…In this portion, to measure the performance and effectiveness of the proposed model, we used evaluation parameters often used for abnormal activity detection [ 6 , 12 , 59 ], such as the AUC and the receiver operating characteristic curve. We also evaluate our proposed model using the recall, F1 score, and precision.…”

Section: Resultsmentioning

confidence: 99%

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Ullah

Hussain

et al. 2021

Sensors

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Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model’s effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

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Section: Resultsmentioning

confidence: 99%

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Ullah

Hussain

et al. 2021

Sensors

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“…Table 3 shows the results obtained by using KTH, UCF11, and KISA Datasets. [28] R3D RGB 76.67 Zhang et al [29] C3D RGB 78.66 Kamoona et al [30] C3D RGB 79.49 GCN-Annmaly [31] C3D RGB 81.08 MIST [32] C3D RGB 81.40 MlST [32] I3D RGB 82.30 Wu et al [33] I3D RGB 82.44 Tian et al [34] C3D RGB 83.28 Tian et al [34] I3D RGB 84.03 Recently, Convolutional 3D (C3D), I3D, and Residual 3D (R3D) methods are mainly used for action recognition algorithms. In Table 3, I3D and C3D showed high performance in the public dataset.…”

Section: Resultsmentioning

confidence: 99%

Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians

Kim¹,

Kim²,

Mok³

et al. 2021

Applied Sciences

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In spite of excellent performance of deep learning-based computer vision algorithms, they are not suitable for real-time surveillance to detect abnormal behavior because of very high computational complexity. In this paper, we propose a real-time surveillance system for abnormal behavior analysis in a closed-circuit television (CCTV) environment by constructing an algorithm and system optimized for a CCTV environment. The proposed method combines pedestrian detection and tracking to extract pedestrian information in real-time, and detects abnormal behaviors such as intrusion, loitering, fall-down, and violence. To analyze an abnormal behavior, it first determines intrusion/loitering through the coordinates of an object and then determines fall-down/violence based on the behavior pattern of the object. The performance of the proposed method is evaluated using an intelligent CCTV data set distributed by Korea Internet and Security Agency (KISA).

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“…This indicates that the attention mechanism derived from 65.51 -Sultani et al [3] 75.41 13.9 Dubey et al [4] 76.67 -I3D baseline 78.62 14.6 Zhang et al [5] 78.66 -Zhu et al [6] 79 -Zhong et al [7] 82.12 -Proposed Model 82.12 34.1…”

Section: B Ablation Studymentioning

confidence: 96%

“…Furthermore, no earlier approaches [18], [26], [23], [27], [7] address the task of anomaly detection and classification jointly. As a result, there is no significant progess in anomaly classification task.…”

Section: Related Workmentioning

confidence: 99%

Weakly-supervised Joint Anomaly Detection and Classification

Majhi

Das

Brémond

et al. 2021

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

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Anomaly activities such as robbery, explosion, accidents, etc. need immediate actions for preventing loss of human life and property in real world surveillance systems. Although the recent automation in surveillance systems are capable of detecting the anomalies, but they still need human efforts for categorizing the anomalies and taking necessary preventive actions. This is due to the lack of methodology performing both anomaly detection and classification for real world scenarios. Thinking of a fully automatized surveillance system, which is capable of both detecting and classifying the anomalies that need immediate actions, a joint anomaly detection and classification method is a pressing need. The task of joint detection and classification of anomalies becomes challenging due to the unavailability of dense annotated videos pertaining to anomalous classes, which is a crucial factor for training modern deep architecture. Furthermore, doing it through manual human effort seems impossible. Thus, we propose a method that jointly handles the anomaly detection and classification in a single framework by adopting a weakly-supervised learning paradigm. In weakly-supervised learning instead of dense temporal annotations, only video-level labels are sufficient for learning. The proposed model is validated on a large-scale publicly available UCF-Crime dataset, achieving state-of-the-art results. The source code and models will be available at https://github.com/ snehashismajhi/JointDetectClassify.

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3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos

Cited by 36 publications

References 16 publications

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

Real-Time Surveillance System for Analyzing Abnormal Behavior of Pedestrians

Weakly-supervised Joint Anomaly Detection and Classification

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