Fuzzy Logic Based Human Activity Recognition in Video Surveillance Applications

Abdelhedi, Slim; Wali, Ali; Alimi, Adel M.

doi:10.1007/978-3-319-29504-6_23

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…The studies on activity recognition are performed using various traditional and modern machine learning methods [16]- [20]. Among the most popular traditional machine learning techniques are support vector machine (SVM), knearest neighbor (kNN), and artificial neural network (ANN).…”

Section: Introductionmentioning

confidence: 99%

Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

Ascioglu

Şenol

2020

IEEE Access

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Human activity monitoring and recognition systems assist experts in evaluating various health problems including obesity, cardiac diseases and, sports injury detection. However, these systems have two challenging points; monitoring activities for outdoor applications and extracting relevant features using hand-crafted techniques from multi-dimensional and large datasets. To address these challenges, we have focused on new dataset generation for activity recognition, a novel design of a sensor-based wireless activity monitoring system, and its application to deep learning neural networks. The designed monitoring system consists of one master and four slave devices, and can collect and record acceleration and gyroscope information. The slave devices were attached on arm, chest, thigh, and shank areas of the human body. Activity data were collected and recorded from sixty healthy people for thirteen activity types including drink from cup and cleaning table. These activities were divided into three activity categories as basic, complex, and all, which is the combination of basic and complex activities. Obtained datasets were fed into deep learning neural networks namely convolutional neural network (CNN), long-short term memory (LSTM) neural networks, and convolutional LSTM (ConvLSTM) neural networks. The performance of each neural network for each category type was separately examined. The results show that ConvLSTM outperforms CNN and LSTM as far as activity recognition is concerned.

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Section: Introductionmentioning

confidence: 99%

Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

Ascioglu

Şenol

2020

IEEE Access

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“…The advancement of image representation approaches and classification methods in visionbased activity recognition literature follows the research trajectory of local, global, and depthbased activity representation methods. Other approaches that being discussed in the literature for human activity detection can be categorized as video-based [11], fuzzy-based [12], trajectorybased [13], hierarchically based [14], data mining based, and color histogram-based suspicious movement detection and tracking [15]. The unusual activity detection process is typically composed of four steps, scene segmentation, feature extraction, monitoring, and human behaviour detection from the video streams.…”

Section: Related Workmentioning

confidence: 99%

Automatic Unusual Activities Recognition Using Deep Learning in Academia

Ramzan¹,

Abid²,

Awan³

2022

Computers, Materials &Amp; Continua

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In the current era, automatic surveillance has become an active research problem due to its vast real-world applications, particularly for maintaining law and order. A continuous manual monitoring of human activities is a tedious task. The use of cameras and automatic detection of unusual surveillance activity has been growing exponentially over the last few years. Various computer vision techniques have been applied for observation and surveillance of real-world activities. This research study focuses on detecting and recognizing unusual activities in an academic situation such as examination halls, which may help the invigilators observe and restrict the students from cheating or using unfair means. To the best of our knowledge, this is the first research work in this area that develops a dataset for unusual activities in the examination and proposes a deep learning model to detect those unusual activities. The proposed model has been named Automatic Unusual Activity Recognition (AUAR), which employs motion-based frame extraction approaches to extract key-frames and then applies advanced deep learning Convolutional Neural Network algorithm with diverse configurations. The evaluation using standard performance measures confirm that the AUAR model outperforms the already proposed approaches for unusual activity recognition. Apart from evaluating the proposed model on the examination dataset, we also apply AUAR on Violent and Movies datasets, widely used in the relevant literature to detect suspicious activities. The results reveal that AUAR performs well on various data sets compared to existing state-of-the-art models.

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“…This dataset can be used to investigate major challenges of background subtraction such as shadows, illumination variations, sleeping foreground objects, noise, camouflage, and moved background objects. Abdelhedi et al [191] proposed an automatic fall detection system in order to monitor children falls in the kindergarten and used kindergarten video dataset for evaluation purpose.…”

Section: Kindergarten Video Datasetmentioning

confidence: 99%

A Comprehensive Survey of Video Datasets for Background Subtraction

Kalsotra

Arora

2019

IEEE Access

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Background subtraction is an effective method of choice when it comes to detection of moving objects in videos and has been recognized as a breakthrough for the wide range of applications of intelligent video analytics (IVA). In recent years, a number of video datasets intended for background subtraction have been created to address the problem of large realistic datasets with accurate ground truth. The use of these datasets enables qualitative as well as quantitative comparisons and allows benchmarking of different algorithms. Finding the appropriate dataset is generally a cumbersome task for an exhaustive evaluation of algorithms. Therefore, we systematically survey standard video datasets and list their applicability for different applications. This paper presents a comprehensive account of public video datasets for background subtraction and attempts to cover the lack of a detailed description of each dataset. The video datasets are presented in chronological order of their appearance. Current trends of deep learning in background subtraction along with top-ranked background subtraction methods are also discussed in this paper. The survey introduced in this paper will assist researchers of the computer vision community in the selection of appropriate video dataset to evaluate their algorithms on the basis of challenging scenarios that exist in both indoor and outdoor environments.INDEX TERMS Background model, background subtraction, challenges, datasets, deep neural networks, foreground, intelligent video analytics (IVA), video frames.

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Fuzzy Logic Based Human Activity Recognition in Video Surveillance Applications

Cited by 13 publications

References 12 publications

Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

Design of a Wearable Wireless Multi-Sensor Monitoring System and Application for Activity Recognition Using Deep Learning

Automatic Unusual Activities Recognition Using Deep Learning in Academia

A Comprehensive Survey of Video Datasets for Background Subtraction

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