MRKFE: Designing cascaded map reduce algorithm for key frame extraction

Deshmane, Praveen

doi:10.1109/icices.2017.8070719

Cited by 1 publication

(2 citation statements)

References 9 publications

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“…The main reason behind suggesting YOLOV3 is the experiments result in terms of accuracy. And video can comprise 20 to 30 frames for each second [25]. This indicates that YOLOV3 performance to process video which is 41.6 FPS is enough to process video.…”

Section: Resultsmentioning

confidence: 99%

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Developing Traﬃc Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road

Bekele

Kekeba

2020

Preprint

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The traﬃc system is one of the core requirements of a civilized world and the development of the country depends on it in many aspects. In Ethiopia, the number of vehicles and pedestrians is increasing at a high rate from time to time. Excessive numbers of traﬃc on roads and improper control of traﬃc create traﬃc congestion. Uncontrolled traﬃc congestion hinders the transportation of goods and commuters from place to place and increases the volume of carbon emitted into the air. It can also either hampers or stagnates schedule, business, and commerce. Many images and video processing approaches have been researched in the literature on how to detect traﬃc congestion. One such approach is that of using background and foreground subtraction, convolutional neural network, and Average frame diﬀerence and deep learning method used to detect traﬃc congestion from diﬀerent video sources. From the review one-stage object detector identiﬁed as the best methods to detect traﬃc congestion with acceptable accuracy and speed. In this study one-stage object detectors are used to detect traﬃc congestion from recorded video. Data is collected from diﬀerent video footage and frames extracted from videos to prepare a dataset for the thesis. The extracted frames were labeled manually as congested and uncongested. To train, the models pre-trained weights were used. YOLOV3 and YOLOV5 model used for experimentation. Accuracy and speed metrics used to evaluate the performance of the models. A YOLOV3 model achieved 41.6 FPS and 68.6 % mAP on a testing dataset.

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Section: Resultsmentioning

confidence: 99%

“…The authors have extracted frames from surveillance videos to prepare the training and testing data set needed for the experiments. This is because video can comprise 20 to 30 frames for each second [25]. One frame per second extracted from the video clips and resized to 500 x 500 pixels.…”

Section: Data Set Preparationmentioning

confidence: 99%

Developing Traﬃc Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road

Bekele

Kekeba

2020

Preprint

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MRKFE: Designing cascaded map reduce algorithm for key frame extraction

Cited by 1 publication

References 9 publications

Developing Traﬃc Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road

Developing Traﬃc Congestion Detection Model Using Deep Learning Approach: A Case Study of Addis Ababa City Road

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