Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Ammar, Sirine; Bouwmans, Thierry; Zaghden, Nizar; Néji, Mahmoud

doi:10.1049/iet-ipr.2019.0769

Cited by 33 publications

(20 citation statements)

References 54 publications

(77 reference statements)

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“…Precision, F1-score, accuracy, recall, sensitivity, equal error rate (EER), specificity, and receiver operating curve (ROC) are also frequently used as metrics in this area. San Francisco cabspotting: [115] SBHAR: [46] SD-OCT: [17] Sentence polarity: [68] ShanghAaiTech: [41,75] SIXray: [91] Spectralis OCT: [26] SWaT system: [93] SVHN: [50,62] TalkingData AdTracking: [113,67] Tennessee eastman: [16,28] Texas coast: [27] Thyroid: [132] UBA: [96] UCI: [38,126] UCSD: [21,22,37,39,41,54,64,65,122,74,75,79,90,107,109] Udacity: [56,61] UMN: [39,43,64,65,74,90,107] UNSW-NB15: [110] VIRAT: [81] WADI test-bed: [93] WOA13 month...…”

Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

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Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data that represents anomalous behaviour. As anomalous behaviour is usually costly (or dangerous) for a system, it is difficult to gather enough data that represents such behaviour. This, in turn, makes it difficult to develop and evaluate anomaly detection techniques. Recently, generative adversarial networks (GANs) have attracted a great deal of attention in anomaly detection research, due to their unique ability to generate new data. In this paper, we present a systematic literature review of the applications of GANs in anomaly detection, covering 128 papers on the subject. The goal of this review paper is to analyze and summarize: (1) which anomaly detection techniques can benefit from certain types of GANs, and how, (2) in which application domains GAN-assisted anomaly detection techniques have been applied, and (3) which datasets and performance metrics have been used to evaluate these techniques. Our study helps researchers and practitioners to find the most suitable GAN-assisted anomaly detection technique for their application. In addition, we present a research roadmap for future studies in this area.

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Section: Rq4: Which Type Of Data Instance and Datasets Are Most Commo...mentioning

confidence: 99%

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Sabuhi¹,

Zhou²,

Bezemer³

et al. 2021

Preprint

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show abstract

“…As can be seen, up to 25 joints are described for a skeleton, where a joint is defined by its (x, y) coordinates inside the RGB frame. The identified joints correspond to-nose (0), neck (1), right/left shoulder, elbow, wrist (2-7), hips middle point ( 8), right/left hip, knee, ankle, eye, ear, big toe, small toes, and heel (9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24). While joint positions alone do not provide useful information, due to their strict correlation to the video they are extracted from, they can still be used to generate a detailed description of body movements, via the feature extraction module.…”

Section: Skeleton Joint Generationmentioning

confidence: 99%

“…For example, algorithms for separating background from foreground, for example, References [ 20 , 21 , 22 ], are often used, as pre-processing stage, both to detect the objects of interest in the scene and to have a reference model of the background and its variations over time. Another example are the tracking algorithms, for example, Reference [ 10 , 23 ], which are used to analyze moving objects. Among these collaborative algorithms, person re-identification ones, for example, References [ 24 , 25 ], play a key role, especially in security, protection, and prevention areas.…”

Section: Introductionmentioning

confidence: 99%

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Avola

Cinque

Fagioli

et al. 2020

Sensors

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Person re-identification is concerned with matching people across disjointed camera views at different places and different time instants. This task results of great interest in computer vision, especially in video surveillance applications where the re-identification and tracking of persons are required on uncontrolled crowded spaces and after long time periods. The latter aspects are responsible for most of the current unsolved problems of person re-identification, in fact, the presence of many people in a location as well as the passing of hours or days give arise to important visual appearance changes of people, for example, clothes, lighting, and occlusions; thus making person re-identification a very hard task. In this paper, for the first time in the state-of-the-art, a meta-feature based Long Short-Term Memory (LSTM) hashing model for person re-identification is presented. Starting from 2D skeletons extracted from RGB video streams, the proposed method computes a set of novel meta-features based on movement, gait, and bone proportions. These features are analysed by a network composed of a single LSTM layer and two dense layers. The first layer is used to create a pattern of the person’s identity, then, the seconds are used to generate a bodyprint hash through binary coding. The effectiveness of the proposed method is tested on three challenging datasets, that is, iLIDS-VID, PRID 2011, and MARS. In particular, the reported results show that the proposed method, which is not based on visual appearance of people, is fully competitive with respect to other methods based on visual features. In addition, thanks to its skeleton model abstraction, the method results to be a concrete contribute to address open problems, such as long-term re-identification and severe illumination changes, which tend to heavily influence the visual appearance of persons.

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“…Ammar [13] developed Deep Detector Classifier (DeepDC) for moving objects segmentation and classification in video surveillance. The developed DeepDC employed and validated Deep Sphere, identified the anomalous cases in spatial and temporal context performed foreground objects segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The object detection is considered as challenging step in the video analytics and it is an important process to analyze, track, and match the objects present in the videos [6-International Journal of Intelligent Engineering and Systems, Vol. 13 8]. In this study, the DOS scheme is used to segment the object in video and this scheme does not assume any prior knowledge about location and number of objects.…”

Section: Introductionmentioning

confidence: 99%

Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Raghavulu¹,

Prasad²

2020

IJIES

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Video analytics is widely used to automatically analyze the videos to extract the required information and detect the various events object identification and traffic analysis. The segmentation of the image is referring to extracting the required region from an image. The major objective of the segmentation process is to cluster the images without being affected by the noises. The detection of the moving objects is a challenging task in video analysis due to the dynamic background of the video. The major drawback of the existing Kernel Fuzzy C-Means (KFCM) clustering is initialization of random centroids which increases the execution time to identify the segmented portions. In this research, the Grey Wolf Optimization (GWO) algorithm used to initialize the centroids of required clusters in KFCM and Level Set (LS) Algorithm is used to segment the objects in video sequence. The proposed KFCM-GWO-LS is implemented for moving and static object detection in the videos obtained from SBM-RGBD dataset. For object detection, determining central clusters are important which is performed by using KFCM. GWO helps in finding the best centroids clusters by matching with KFCM. The centroids clusters are segmented by using LS algorithm which undergoes over segmentation problem that is overcome by GWO. As all the three techniques are dependent on one another hybrid of all these techniques obtains better results. The proposed KFCM-GWO-LS is evaluated in terms of Recall, Specificity, Precision and F-measure and the experimental results showed that the proposed method improved the system performance from 0.3 % to 14.35 % compared to existing methods Multi-Sensor Scheduler Algorithm and Statistical Inference Theory model.

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Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Cited by 33 publications

References 54 publications

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review

Bodyprint—A Meta-Feature Based LSTM Hashing Model for Person Re-Identification

Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

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