Deep learning and handcrafted features for one-class anomaly detection in UAV video

Chriki, Amira; Touati, Haifa; Snoussi, Hichem; Kamoun, Farouk

doi:10.1007/s11042-020-09774-w

Cited by 45 publications

(22 citation statements)

References 47 publications

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“…Another related line of research addresses anomaly detection in video surveillance. In [39], a method has been proposed for surveillance based on Unmanned Aerial Vehicles (UAVs). To extract features from UAV videos, they utilized a 3-way process consisting of the use of a CNN, Histogram of Oriented Gradient (HOG), and HOG3D.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

Mehmood

2021

Applied Sciences

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The increasing demand for surveillance systems has resulted in an unprecedented rise in the volume of video data being generated daily. The volume and frequency of the generation of video streams make it both impractical as well as inefficient to manually monitor them to keep track of abnormal events as they occur infrequently. To alleviate these difficulties through intelligent surveillance systems, several vision-based methods have appeared in the literature to detect abnormal events or behaviors. In this area, convolutional neural networks (CNNs) have also been frequently applied due to their prevalence in the related domain of general action recognition and classification. Although the existing approaches have achieved high detection rates for specific abnormal behaviors, more inclusive methods are expected. This paper presents a CNN-based approach that efficiently detects and classifies if a video involves the abnormal human behaviors of falling, loitering, and violence within uncrowded scenes. The approach implements a two-stream architecture using two separate 3D CNNs to accept a video and an optical flow stream as input to enhance the prediction performance. After applying transfer learning, the model was trained on a specialized dataset corresponding to each abnormal behavior. The experiments have shown that the proposed approach can detect falling, loitering, and violence with an accuracy of up to 99%, 97%, and 98%, respectively. The model achieved state-of-the-art results and outperformed the existing approaches.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

Mehmood

2021

Applied Sciences

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“…They adopted a two-stream DenseNet to extract spatial and temporal features from a self-collected dataset. Chriki et al [52] have proposed a method for surveillance with the help of unmanned aerial vehicles (UAVs). It combined the use of CNN with hand-crafted methods (HOG and HOG3D) for feature extraction.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection

Mehmood

2021

Sensors

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The continuous development of intelligent video surveillance systems has increased the demand for enhanced vision-based methods of automated detection of anomalies within various behaviors found in video scenes. Several methods have appeared in the literature that detect different anomalies by using the details of motion features associated with different actions. To enable the efficient detection of anomalies, alongside characterizing the specificities involved in features related to each behavior, the model complexity leading to computational expense must be reduced. This paper provides a lightweight framework (LightAnomalyNet) comprising a convolutional neural network (CNN) that is trained using input frames obtained by a computationally cost-effective method. The proposed framework effectively represents and differentiates between normal and abnormal events. In particular, this work defines human falls, some kinds of suspicious behavior, and violent acts as abnormal activities, and discriminates them from other (normal) activities in surveillance videos. Experiments on public datasets show that LightAnomalyNet yields better performance comparative to the existing methods in terms of classification accuracy and input frames generation.

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“…The work in [104] presents another strategy for anomaly detection through UAVs. The main contribution of the proposal is the comparison of four sets of features.…”

Section: Uav Surveillancementioning

confidence: 99%

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

et al. 2021

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In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

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Deep learning and handcrafted features for one-class anomaly detection in UAV video

Cited by 45 publications

References 47 publications

Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

LightAnomalyNet: A Lightweight Framework for Efficient Abnormal Behavior Detection

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

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