Hybrid deep learning and HOF for Anomaly Detection

Hamdi, Slim; Bouindour, Samir; Loukil, Kais; Snoussi, Hichem; Abid, Mohamed

doi:10.1109/codit.2019.8820352

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…This process is performed using Equation ( 1), where G is the activation parameter and v is the weight of each neuron. (1) .…”

Section: Process Of Lbdbnp For Predicting Crime Eventsmentioning

confidence: 99%

“…In recent years, crime is the most crucial issue due to the growth of the population and increase in complexity and intensity. 1 Hence, detecting crimes manually is a challenging task, so that crime prediction schemes were digitalized with the use of advanced technology. 2 Here, the anomalies are predicted by designing the machine learning (ML) or deep learning (DL) approaches.…”

Section: Introductionmentioning

confidence: 99%

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Crime activities prediction system in video surveillance by an optimized deep learning framework

Kumar¹,

Reddy²

2022

Concurrency and Computation

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The video surveillance technology is used to find crime events in public places and capture live public events. Hence, detecting the criminalist before the crime actions is the most needed event to catch the criminalist. However, the presence of noise content in the trained video has raised the difficulties in crime specification by maximizing the complexity range of the data. To overcome this issue, this research has designed a novel lion-based deep belief neural paradigm (LbDBNP) to identify criminals by their activities and handling tools. Initially, three types of datasets were trained to the system then the training flaws were eliminated in the preprocessing layer. Hereafter, the cleaned data is imported to the classification module to detect the crime events present in the video. Subsequently, the designed model is implemented using the Python framework in Windows 10 platform. To evaluate the efficiency of the designed model, the attack is launched in the proposed model after those metrics are calculated. In addition, the robustness of the designed system is verified by three datasets, such as UCSDped1, UCSDped2, and avenue crime. Also, the key parameters of the designed model have been evaluated and compared with other existing schemes to verify the proposed model's robustness by achieving the finest outcomes.

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“…This process is performed using Equation ( 1), where G is the activation parameter and v is the weight of each neuron. (1) .…”

Section: Process Of Lbdbnp For Predicting Crime Eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crime activities prediction system in video surveillance by an optimized deep learning framework

Kumar¹,

Reddy²

2022

Concurrency and Computation

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“…Several methods for detecting human activity in a situation have been proposed [4]. As illustrated in Figure (2), there are two primary types of techniques that may recognize normal and abnormal occurrences in crowded and uncrowded situations. The first is a handcrafted features-based technique, which relies on extracting a collection of features such as motion or texture, making it more suitable for cluttered scenes.…”

Section: Fig1 -The Block Diagram Of the Main Stages Of The Unusual Ac...mentioning

confidence: 99%

“…Finding such anomalies in videos is critical for a variety of applications ranging from automatic quality control to visual monitoring settings such as jails and schools, as well as banning inappropriate violence in children's movies. The majority of work in event analysis is centered on two primary paths [2]: the first is video sequence walking or jogging, and the other is anomalous detection, which focuses on recognizing rare or unexpected events such as violent behaviors. Local anomalous detection and global anomalous detection are two types of anomalous detection [3].…”

Section: Introductionmentioning

confidence: 99%

Unusual Activity Detection in Surveillance Video Scene: Review

Mahdi

Mohammed²,

Jafer

2021

J. Al-Qadisiyah Comp. Sci. Math.

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Abnormal activity may indicate threats and risks to others. An anomaly can be defined as something that deviates from what is expected, common, or normal. Because it is difficult to continuously monitor public spaces, intelligent video surveillance is necessary, which can monitor human actions in real-time and categorize them as ordinary or exceptional, as well as create an alarm. Human activities in public and sensitive regions such as bus stations, airports, railway stations, malls, banks, universities, car parks, roads, and other regions can be monitored using visual surveillance to prevent crime, theft, terrorism, vandalism, accidents, and other suspicious activities. This makes video surveillance a key to increasing public security. The main objective of event discovery is to discover the occurrence of events and categorize them into normal or abnormal actions. This discovery requires identifying and tracking objects and then learning what is going around those tracked objects. Recent research is based on one of two technologies: handcrafted features and deep learning models. Handmade features are based on extracting low-level features, and their strength is based on selecting the best features, that produce the best results. After the success of deep learning techniques for classifying images, the researchers examined the ability of deep learning techniques to detect, which bypasses the manual step of feature extraction and works directly with images. This paper presents a survey of both handmade and deep learning models to detect abnormal events.

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Fine-Tuning a Pre-trained CAE for Deep One Class Anomaly Detection in Video Footage

Hamdi

Snoussi

Abid

2021

Communications in Computer and Information Science

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In recent years, abnormal event detection in video surveillance has become a very important task mainly treated by deep learning methods taken into account many challenges. However, these methods still not trained on an anomaly detection based objective which proves their ineffectiveness in such a problem. In this paper, we propose an unsupervised method based on a new architecture for deep one class of convolutional auto-encoders (CAEs) for representing a compact Spatio-temporal feature for anomaly detection. Our CAEs are constructed by added deconvolutions layers to the CNN VGG 16. Then, we train our CAEs for a one-class training objective by fine-tuning our model to properly exploit the richness of the dataset with which CNN was trained. The first CAE is trained on the original frames to extract a good descriptor of shapes and the second CAE is learned using optical flow representations to provide a strength description of motion between frames. For this purpose, we define two loss functions, compactness loss and representativeness loss for training our CAEs architectures not only to maximize the inter-classes distance and to minimize the intra-class distance but also to ensure the tightness and the representativeness of features of normal images. We reduce features dimensions by applying a PCA (Principal Component Analyser) to combine our two descriptors with a Gaussian classifier for abnormal Spatio-temporal events detection. Our method has a high performance in terms of reliability and accuracy. It achieved abnormal event detection with good efficiency in challenging datasets compared to state-of-the-art methods.

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Hybrid deep learning and HOF for Anomaly Detection

Cited by 8 publications

References 21 publications

Crime activities prediction system in video surveillance by an optimized deep learning framework

Crime activities prediction system in video surveillance by an optimized deep learning framework

Unusual Activity Detection in Surveillance Video Scene: Review

Fine-Tuning a Pre-trained CAE for Deep One Class Anomaly Detection in Video Footage

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