Deep learning-based real-world object detection and improved anomaly detection for surveillance videos

Deep learning has proven to be a landmark computing approach to the computer vision domain. Hence, it has been widely applied to solve complex cognitive tasks like the detection of anomalies in surveillance videos. Anomaly detection in this case is the identification of abnormal events in the surveillance videos which can be deemed as security incidents or threats. Deep learning solutions for anomaly detection has outperformed other traditional machine learning solutions. This review attempts to provide holistic benchmarking of the published deep learning solutions for videos anomaly detection since 2016. The paper identifies, the learning technique, datasets used and the overall model accuracy. Reviewed papers were organised into five deep learning methods namely; autoencoders, continual learning, transfer learning, reinforcement learning and ensemble learning. Current and emerging trends are discussed as well.

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Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network

PLoS ONE ◽

10.1371/journal.pone.0256290 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256290

Author(s):

Taehan Koo ◽

Moon Hwan Kim ◽

Mihn-Sook Jue

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Sensitivity And Specificity ◽

Real World ◽

Data Model ◽

Fungal Infections ◽

Curve Analysis ◽

Microscopic Images

Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to evaluate a complete sample; additionally, it possesses the disadvantage of inconsistent reliability as the accuracy of the reading may differ depending on the performer’s skill. This study aims at detecting hyphae more quickly, conveniently, and consistently through deep learning using images obtained from microscopy used in real-world practice. An object detection convolutional neural network, YOLO v4, was trained on microscopy images with magnifications of 100×, 40×, and (100+40)×. The study was conducted at the Department of Dermatology at Veterans Health Service Medical Center, Seoul, Korea between January 1, 2019 and December 31, 2019, using 3,707 images (1,255 images for training, 1,645 images for testing). The average precision was used to evaluate the accuracy of object detection. Precision recall curve analysis was performed for the hyphal location determination, and receiver operating characteristic curve analysis was performed on the image classification. The F1 score, sensitivity, and specificity values were used as measures of the overall performance. The sensitivity and specificity were, respectively, 95.2% and 100% in the 100× data model, and 99% and 86.6% in the 40× data model; the sensitivity and specificity in the combined (100+40)× data model were 93.2% and 89%, respectively. The performance of our model had high sensitivity and specificity, indicating that hyphae can be detected with reliable accuracy. Thus, our deep learning-based autodetection model can detect hyphae in microscopic images obtained from real-world practice. We aim to develop an automatic hyphae detection system that can be utilized in real-world practice through continuous research.

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Improved anomaly detection in surveillance videos based on a deep learning method

2018 8th Conference of AI & Robotics and 10th RoboCup Iranopen International Symposium (IRANOPEN) ◽

10.1109/rios.2018.8406634 ◽

2018 ◽

Cited By ~ 4

Author(s):

Ali Khaleghi ◽

Mohammad Shahram Moin

Keyword(s):

Deep Learning ◽

Anomaly Detection ◽

Learning Method ◽

Surveillance Videos

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An Optimized CNN based Real World Anomaly Detection in Surveillance Videos

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1074.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 465-473

Keyword(s):

Artificial Intelligence ◽

Genetic Algorithm ◽

Anomaly Detection ◽

Real World ◽

Large Scale ◽

Detection System ◽

Adaptive Genetic Algorithm ◽

Surveillance Video ◽

Surveillance Videos ◽

Anomaly Detection System

Anomaly detection in automated surveillance video is an extremely monotonous process for monitoring for crowded scenes and surveillance videos are capable to incarcerate a mixture of sensible anomalies. An appropriate machine learning technique can help to train the Anomaly Detection System (ADS) in identifying anomalous activities during surveillance. To this end, we present an anomaly detection system that can be used as a tool for anomaly detection in surveillance videos using the concept of artificial intelligence. The main intention of the proposed anomaly detection system is to improve the detection time and accuracy by using the concept of Convolutional Neural Network (CNN) as artificial intelligence technique. In this paper we present a CNN based Anomaly Detection System (CNN-ADS), which is the combination of multiple layer of hidden unit with the optimized MSER feature by using Genetic Algorithm (GA). Here CNN is used for classifying the activity into normal and abnormal from the surveillance videos based on the fitness function of GA which is used for the selection of optimal MSER feature sets. Further, Self adaptive genetic algorithm (SAGA) is adopted to efficiently solve optimization problems in the continuous search domain to select the best possible feature to segregate the pattern of normal and abnormal activities. The main contribution of this research is validation of proposed system for the large scale data and we introduce a new large-scale dataset of 128 hours of videos. Dataset consists of 1900 long and untrimmed real-world surveillance videos, with 13 sensible anomalies such as road accident, burglary, fighting, robbery, etc. as well as normal activities. The experimental results of the planned system show that our CNN-ADS for anomaly detection achieve essential improvement on anomaly detection presentation as compared to the state-of-the-art approaches. The dataset is available at: https://webpages.uncc.edu/cchen62/dataset.html. In this paper, to validate the proposed ADS we provide the comparison of existing results of several recent deep learning baselines on anomalous activity detection. The real-time ADS in surveillance video sequences using SAGA based CNN with MSER feature extraction technique is implemented using Image Processing Toolbox within Matlab Software.

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