Video content analysis using convolutional neural networks

Aljarrah, Inad A.; Mohammad, Duaa

doi:10.1109/iacs.2018.8355453

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…VCA can be utilized for constant observation of waste removal at the side of the road or flooding of dustbins. In [17] presents a model wherein video observation content is dissected, utilizing object arrangement. Items showing up in the video are recognized and ordered using a convolutional neural system model.…”

Section: Related Workmentioning

confidence: 99%

“…At present, various models closely resemble autonomous garbage detection systems with the help of several techniques. Image processing via Convolutional Neural Network for classification, detection and categorization of images [9][10][11][12], image classification via Gray level co-occurrence matrix [13] and Gray level Aura Matrix [14], garbage detection using Support Vector Machines [15,16], detection of pools of garbage on roadside via Video Content Analysis using VGGNet [17] and keyframe extraction, Volumetric estimation of garbage in the bin for preventing overflowing using poisson surface reconstruction [11], smart garbage bins for garbage level detection with RFID/IR/ultrasonic sensors [18], detection of unauthorized pooling of garbage using real-world surveillance [19], garbage detection and collection by robotic arms using Artificial Intelligence algorithms and raspberry pi [20], application based on Convulational Neural Network (CNN) to track and report pools of garbage [21], Multilayer Hybrid deep learning method to extract image features using CNN and multilayer perceptrons (MLP) to consolidate image features to classify wastes as recyclable or the others [11], identifying the waste container using radio frequency identification [22,23].…”

Section: Introductionmentioning

confidence: 99%

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A deep learning approach based hardware solution to categorise garbage in environment

Gupta

Joshi

Mukhopadhyay

et al. 2021

Complex Intell. Syst.

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Garbage detection and disposal have become one of the major hassles in urban planning. Due to population influx in urban areas, the rate of garbage generation has increased exponentially along with garbage diversity. In this paper, we propose a hardware solution for garbage segregation at the base level based on deep learning architecture. The proposed deep-learning-based hardware solution SmartBin can segregate the garbage into biodegradable and non-biodegradable using Image classification through a Convolutional Neural Network System Architecture using a Real-time embedded system. Garbage detection via image classification aims for quick and efficient categorization of garbage present in the bin. However, this is an arduous task as garbage can be of any dimension, object, color, texture, unlike object detection of a particular entity where images of objects of that entity do share some similar characteristics and traits. The objective of this work is to compare the performance of various pre-trained Convolution Neural Network namely AlexNet, ResNet, VGG-16, and InceptionNet for garbage classification and test their working along with hardware components (PiCam, raspberry pi, infrared sensors, etc.) used for garbage detection in the bin. The InceptionNet Neural Network showed the best performance measure for the proposed model with an accuracy of 98.15% and a loss of 0.10 for the training set while it was 96.23% and 0.13 for the validation set.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning approach based hardware solution to categorise garbage in environment

Gupta

Joshi

Mukhopadhyay

et al. 2021

Complex Intell. Syst.

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“…The and filters are convolved with the image and result in a gradient of a magnitude that is computed using (1). The gradient direction is computed using (2) [35].…”

Section: Figure 4 Sobel Edge Detector Filtersmentioning

confidence: 99%

“…Video content analysis (VCA) is the method of analyzing video streams to detect and determine temporal and spatial events to find what a video represents and the type of information it has [1]. It is used by different applications that require high security to detect intruders and abnormal events especially fraud actions [2].…”

Section: Introductionmentioning

confidence: 99%

Searching surveillance video contents using convolutional neural network

Mohammad

Aljarrah

Jarrah

2021

IJECE

Self Cite

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Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.

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“…The main challenge is to perform video indexing that should be readily available to the students so they can search the topics that are required. In [17] [18] [19] Video metadata analysis and indexing are the main focus. Video indexing can be done by analyzing audio, video and textual information that appears on the blackboard.…”

Section: Introductionmentioning

confidence: 99%

Automated analysis and indexing of lecture videos

Sreepathy¹

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Video content analysis using convolutional neural networks

Cited by 14 publications

References 12 publications

A deep learning approach based hardware solution to categorise garbage in environment

A deep learning approach based hardware solution to categorise garbage in environment

Searching surveillance video contents using convolutional neural network

Automated analysis and indexing of lecture videos

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