Moving object detection zone using a block‐based background model

Elharrouss, Omar; Abbad, Abdelghafour; Moujahid, Driss; Tairi, Hamid

doi:10.1049/iet-cvi.2017.0136

Cited by 15 publications

(7 citation statements)

References 34 publications

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“…These metrics are presented in Table 2 that illustrate the obtained results comparing with two background modeling methods IMBS-MT [54] and [43] respectively. As shown, the proposed method succeed to modelized the background with good results compared to the other method in the most of dataset videos including HighwayI, Hall&Monitor, sallen, and Foliage.…”

Section: Action Recognition and Summarization Resultsmentioning

confidence: 99%

“…The challenge in video summarization is related to the video understanding and the classification of important sequences of the video, and the importance depends on the types of actions or objects that must be summarized. The complexity and variety of scenes in a video making the foundation of generic methods impossible [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

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A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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Human action recognition and video summarization represent challenging tasks for several computer vision applications including video surveillance, criminal investigations, and sports applications. For long videos, it is difficult to search within a video for a specific action and/or person. Usually, human action recognition approaches presented in the literature deal with videos that contain only a single person, and they are able to recognize his action. This paper proposes an effective approach to multiple human action detection, recognition, and summarization. The multiple action detection extracts human bodies' silhouette, then generates a specific sequence for each one of them using motion detection and tracking method. Each of the extracted sequences is then divided into shots that represent homogeneous actions in the sequence using the similarity between each pair frames. Using the histogram of the oriented gradient (HOG) of the Temporal Difference Map (TDMap) of the frames of each shot, we recognize the action by performing a comparison between the generated HOG and the existed HOGs in the training phase which represents all the HOGs of many actions using a set of videos for training. Also, using the TDMap images we recognize the action using a proposed CNN model. Action summarization is performed for each detected person. The efficiency of the proposed approach is shown through the obtained results for mainly multi-action detection and recognition.

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Section: Action Recognition and Summarization Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A combined multiple action recognition and summarization for surveillance video sequences

et al. 2020

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“…For the sequential-based approaches metrics used for evaluation are unlike CNN-based methods. In order to evaluate efficiency of the proposed methods, researchers use some evaluation measures including mean squared error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [73]. For example, Zeng et al [18] use these evaluation parameters to demonstrate the obtained results of repairing of scratch and text in the images.…”

Section: Evaluation and Discussionmentioning

confidence: 99%

Image Inpainting: A Review

Elharrouss

Almaadeed

Al‐Maadeed

et al. 2019

Neural Process Lett

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268

100

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Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has gained even more popularity because of the recent development in image processing techniques. With the improvement of image processing tools and the flexibility of digital image editing, automatic image inpainting has found important applications in computer vision and has also become an important and challenging topic of research in image processing. This paper is a brief review of the existing image inpainting approaches we first present a global vision on the existing methods for image inpainting. We attempt to collect most of the existing approaches and classify them into three categories, namely, sequential-based, CNN-based and GAN-based methods. In addition, for each category, a list of methods for the different types of distortion on the images is presented. Furthermore, collect a list of the available datasets and discuss these in our paper. This is a contribution for digital image inpainting researchers trying to look for the available datasets because there is a lack of datasets available for image inpainting. As the final step in this overview, we present the results of real evaluations of the three categories of image inpainting methods performed on the datasets used, for the different types of image distortion. In the end, we also present the evaluations metrics and discuss the performance of these methods in terms of these metrics. This overview can be used as a reference for image inpainting researchers, and it can also facilitate the comparison of the methods as well as the datasets used. The main contribution of this paper is the presentation of the three categories of image inpainting methods along with a list of available datasets that the researchers can use to evaluate their proposed methodology against.

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“…It uses multi-resolution, foveated architecture which can train a larger dataset in lesser time, while 3D CNN single steam [18] models fuses the useful information of the frames having RGB data or other types of frames with the optical flow field to get desired results. There are different approaches based on the CNN model like frequency-based [19], motion-based [20], optical flow-based [14], color-based [21] developed by the researcher. The main drawback of this CNN-based approach is the training time required is very large.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent Video Analytics For Human Action Detection: A Deep Learning Approach With Transfer Learning

Begampure¹,

Jadhav²

2022

IJCDS

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Human actions consist of a sequence of similar patterns which are difficult to classify using traditional image processing algorithms. Video analytics is a major research area that adds brains to eyes which means analytics to the camera. It monitors the video contents and extracts intelligent information from it. The human action analysis and its detection is a challenging task. The proposed method focuses on detection of normal human activity using Long-Short Term Memory (LSTM) as a deep neural architecture. The pre-processing technique of redundant frame detection along with pre-trained Convolutional Neural Network (CNN) is implemented for classifying the activities efficiently. Transfer learning approach is used followed by Long-Short Term Memory (LSTM) network to generate hybrid framework which further enhances the activity detection. Proposed method shows improvement in accuracy as compared to reference method. This method can be further implemented for on edge processing in embedded platforms for real time applications.

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Moving object detection zone using a block‐based background model

Cited by 15 publications

References 34 publications

A combined multiple action recognition and summarization for surveillance video sequences

A combined multiple action recognition and summarization for surveillance video sequences

Image Inpainting: A Review

Intelligent Video Analytics For Human Action Detection: A Deep Learning Approach With Transfer Learning

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