Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Athanesious, J. Joshan; Vasuhi, S.; Vijayakumar, V.; Christobel, Shiny; Sethuraman, Sibi Chakkaravarthy

doi:10.1049/iet-ipr.2019.0549

Cited by 17 publications

(10 citation statements)

References 15 publications

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“…e light conditions and background of each sample are also different, which fully guarantees the diversity of positive samples. If the classifier can identify these types of vehicles with different backgrounds, it is enough to show that the proposed classifier has strong adaptability [18,19].…”

Section: Sample Collectionmentioning

confidence: 99%

Vehicle Image Detection Method Using Deep Learning in UAV Video

Wang

2022

Computational Intelligence and Neuroscience

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Traditional machine learning algorithms are susceptible to objective factors such as video quality and weather environment in the vehicle detection of Unmanned Aerial Vehicle (UAV) videos, resulting in poor detection results. A vehicle image detection method using deep learning in UAV video is proposed. The algorithm in this paper treats surveillance video as many frames of images for vehicle detection in the image. First, perform HSV (Hue-Saturation-Value) spatial brightness translation operation on the original sample to increase the adaptability to different light conditions and sample diversity. Then, the Single Shot MultiBox Detector (SSD) model framework is used as the basis for vehicle detection. In order to obtain a better feature extraction effect, focus loss is added to the basic SSD for optimization. Finally, the trained network model is used to analyze the UAV video, and the detection performance is analyzed experimentally. The results show that the vehicle detection rate of this algorithm has reached 96.49%. It can ensure that the vehicle is accurately detected from the drone video.

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Section: Sample Collectionmentioning

confidence: 99%

Vehicle Image Detection Method Using Deep Learning in UAV Video

Wang

2022

Computational Intelligence and Neuroscience

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“…The above experimental results are used to test the effect of the algorithm from the visual angle, and the following two classical standard peak signal-to-noise ratio (PSNR) and SSIM are used to evaluate the SR reconstruction effect of the algorithm for video surveillance scene image. PSNR = 20log 10 (255∕RMSE ) (17) where the root MSE of video surveillance scene image reconstruction is RMSE and SSIM.…”

Section: Super Resolution Reconstruction Effect Of Video Surveillance Scene Imagementioning

confidence: 99%

An image enhancement algorithm of video surveillance scene based on deep learning

Shen

Lin

Liu

et al. 2021

IET Image Processing

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Target enhancement is the most important task in a video surveillance system. In order to improve the accuracy and efficiency of target enhancement, and better deal with the subsequent recognition, tracking, behaviour understanding and other processing of targets, a deep learning-based image enhancement algorithm for video surveillance scenes is proposed. First, the super-resolution reconstruction of the image is carried out through the image super-resolution reconstruction method based on the hybrid deep convolutional network to improve the sharpness of the image. Then, for the reconstructed video surveillance scene image, the watershed image enhancement algorithm based on morphology and region merging is used to realize the enhancement of the video surveillance scene image. Deep learning algorithms can improve the accuracy of image enhancement through iterative calculations. Experimental results show that after image enhancement in daytime, night and noisy video surveillance scenes, the maximum enhancement difference rate is less than 0.5%, the cross-linking degree is close to 1, and the average image enhancement time is less than 1.3 s. It can realize image enhancement of video surveillance scenes and improve the image clarity of the video surveillance scene.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Accident detection is performed from a stack of videos using optimization problem and submodularity. Yet another model for abnormal event detection based on an improved optical flow method, specifically for traffic video surveillance is developed by Athanesious et al [4]. The method incorporated is super-oriented optical flow clustering.…”

Section: Surveillance Video Summarisation Frameworkmentioning

confidence: 99%

An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

Sreeja

Kovoor

2021

IET Computer Vision

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Surveillance video summarisation is characterised by extracting video segments containing abnormal events from surveillance video footages. Accurate identification of abnormal events from surveillance footages is of paramount importance in surveillance video summarisation. Accordingly, the proposed framework builds an aggregated convolutional recurrent model that can precisely detect the suspicious events in a surveillance footage, by employing a supervised learning which is found to yield better results compared with unsupervised counterparts. The preliminary stage in the model is a multilayer Convolutional Neural Network for frame-level feature extraction followed by stacked bidirectional Gated Recurrent Unit for sequence-level feature extraction and classification. Since the video clips used for training are not implicit to surveillance, a block-based approach for testing on surveillance videos is proposed. The results evaluated on two custom datasets, Streets and Campus, prove that the proposed model produces remarkable results leveraging the properties of bidirectional GRU with supervised learning. Extensive experimental analysis on selection of optimum architecture is conducted which substantiates the significance of stacked bidirectional GRUs over unidirectional ones. Additionally, qualitative results ensure that summaries produced are concise, representative, complete, diverse and informative. Moreover, comparison of the performance of the proposed model with state of the art certainly proves the superiority of the proposed model.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Cited by 17 publications

References 15 publications

Vehicle Image Detection Method Using Deep Learning in UAV Video

Vehicle Image Detection Method Using Deep Learning in UAV Video

An image enhancement algorithm of video surveillance scene based on deep learning

An aggregated deep convolutional recurrent model for event based surveillance video summarisation: A supervised approach

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