Energy Efficient Camera Solution for Video Surveillance

Ahmad, Misbah; Ahmed, Imran; Ullah, Kaleem; Khan, Iqbal; Khattak, Ayesha; Adnan, Awais

doi:10.14569/ijacsa.2019.0100367

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…The top view perspective offers wide coverage, better visibility of the objects in the field of view, and may handle occlusion problems better than frontal view [23] and [24]. Moreover, using a single top view camera is also beneficial in terms of installation expense, energy usage, and human resource (required to monitor multiple cameras) [25]. These models were originally pre-trained using data set captured from normal or frontal view i.e.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

2020

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Image segmentation is considered as a key research topic in the area of computer vision. It is pivotal in a broad range of real-life applications. Recently, the emergence of deep learning drives significant advancement in image segmentation; the developed systems are now capable of recognizing, segmenting, and classifying objects of specific interest in images. Generally, most of these techniques primarily focused on the asymmetric field of view or frontal view objects. This work explores widely used deep learningbased models for person segmentation using top view data set. The first model employed in this work is Fully Convolutional Neural Network (FCN) with Resnet-101 architecture. The network consists of a set of max-pooling and convolution layers to identify pixel-wise class labels and prediction of the mask. The second model is based on FCN called U-Net with Encoder-Decoder architecture. The encoder is mainly comprised of a contracting path, also called an encoder, which captures the context in the image and symmetric expanding path called decoder to enable accurate location. The third model used for top view person segmentation is a DeepLabV3 model also with encoder-decoder architecture. The encoder consists of trained Convolutional Neural Network (CNN) to encode feature maps of the input image. The decoder is used for up-sampling and reconstruction of output using important information extracted by the encoder. All segmentation models are firstly tested using pre-trained models (trained on frontal view data set). To improve the performance, these models are further trained using person data set captured from a top view. The output of all models consists of a segmented person in the top view images. The experimental results reveal the effectiveness and performance of segmentation models by achieving IoU of 83%, 84%, and 86% and mIoU of 80% 82%and 84% for FCN, U-Net, and DeepLabv3 respectively. Furthermore, the discussion is provided for output results with possible future guidelines.

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Section: Introductionmentioning

confidence: 99%

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

2020

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“…The overhead perspective offers a better field of view and overcomes the issues of occlusion, thereby playing a key role in social distance monitoring to compute the distance between peoples. It might help overcome computation, communication load, energy consumption, human resource, and installation costs ( Ahmad et al, 2019 ). This work aims to present a deep learning-based social distance monitoring framework for the public campus environment from an overhead perspective.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based social distance monitoring framework for COVID-19

Ahmed

Ahmad

Rodrigues

et al. 2021

Sustainable Cities and Society

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Highlights The purpose of this work is to provide a deep learning platform for social distance tracking. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is implemented to increase the accuracy of the model. The detection algorithm uses a pre-trained algorithm. To estimate social distance violations between people, we used an approximation of physical distance.

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“…Using single overhead camera may provide an efficient solution for saving energy consumption, human resource, and installation costs. 10 In this work, overhead view person tracking is performed using CNN-based detection and tracking models. For overhead person tracking, Generic Object Tracking Using Regression Networks (GOTURN) algorithm 11 is used which takes advantage of the CNN architecture to track the object in a video sequence with high accuracy and speed.…”

Section: Introductionmentioning

confidence: 99%

“…Using single overhead camera may provide an efficient solution for saving energy consumption, human resource, and installation costs. 10…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural network–based person tracking using overhead views

Ahmad

Khan

Qayum

et al. 2020

International Journal of Distributed Sensor Networks

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In video surveillance, person tracking is considered as challenging task. Numerous computer vision, machine and deep learning–based techniques have been developed in recent years. Majority of these techniques are based on frontal view images/video sequences. The advancement of convolutional neural network reforms the way of object tracking. The network layers of convolutional neural network models trained on a number of images or video sequences improve speed and accuracy of object tracking. In this work, the generalization performance of existing pre-trained deep learning models have investigated for overhead view person detection and tracking, under different experimental conditions. The object tracking method Generic Object Tracking Using Regression Networks (GOTURN) which has been yielding outstanding tracking results in recent years is explored for person tracking using overhead views. This work mainly focused on overhead view person tracking using Faster region convolutional neural network (Faster-RCNN) in combination with GOTURN architecture. In this way, the person is first identified in overhead view video sequences and then tracked using a GOTURN tracking algorithm. Faster-RCNN detection model achieved the true detection rate ranging from 90% to 93% with a minimum false detection rate up to 0.5%. The GOTURN tracking algorithm achieved similar results with the success rate ranging from 90% to 94%. Finally, the discussion is made on output results along with future direction.

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Energy Efficient Camera Solution for Video Surveillance

Cited by 18 publications

References 19 publications

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images

A deep learning-based social distance monitoring framework for COVID-19

Convolutional neural network–based person tracking using overhead views

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