Semi-supervised domain adaptation for pedestrian detection in video surveillance based on maximum independence assumption

Shojaei, Ghazaleh; Razzazi, Farbod

doi:10.1007/s13735-019-00180-z

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…Both individuals and objects together give the potential benefits of detecting interaction between them such as an individual carrying a suspicious baggage [ 84 ], individual throwing a chair [ 3 ]. Some studies attempt to account for both pedestrians and vehicles in the same scene such as cyclists driving on a footpath, pedestrians walking on the road [ 84 , 120 , 147 , 153 ]. In [ 107 ], abnormal behavior is identified by objects like a skateboarder, a vehicle, or a wheelchair on the footpath.…”

Section: Categorization Of Anomalies According To Surveillance Targetsmentioning

confidence: 99%

Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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The current concept of smart cities influences urban planners and researchers to provide modern, secured and sustainable infrastructure and gives a decent quality of life to its residents. To fulfill this need, video surveillance cameras have been deployed to enhance the safety and well-being of the citizens. Despite technical developments in modern science, abnormal event detection in surveillance video systems is challenging and requires exhaustive human efforts. In this paper, we focus on evolution of anomaly detection followed by survey of various methodologies developed to detect anomalies in intelligent video surveillance. Further, we revisit the surveys on anomaly detection in the last decade. We then present a systematic categorization of methodologies for anomaly detection. As the notion of anomaly depends on context, we identify different objects-of-interest and publicly available datasets in anomaly detection. Since anomaly detection is a time-critical application of computer vision, we explore the anomaly detection using edge devices and approaches explicitly designed for them. The confluence of edge computing and anomaly detection for real-time and intelligent surveillance applications is also explored. Further, we discuss the challenges and opportunities involved in anomaly detection using the edge devices.

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Section: Categorization Of Anomalies According To Surveillance Targetsmentioning

confidence: 99%

Anomaly detection using edge computing in video surveillance system: review

Patrikar

Parate

2022

Int J Multimed Info Retr

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“…They found that the log-average miss rate of the algorithm reached 27.6%. Shojaei et al [9] used transfer component analysis and maximum independent domain in pedestrian target detection. The experimental results on the dataset of INRIA showed that the pedestrian target detection algorithm with domain adaptation had less classification error.…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Detection Algorithm for Pedestrians on the Road Based on Image Processing Method

Zhang¹

2023

IJACSA

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Accurate detection of pedestrian targets can effectively improve the performance level of intelligent transportation and surveillance projects. In order to effectively enhance the accuracy of detecting pedestrian targets on the road, this paper first introduced the traditional pedestrian target detection algorithm, proposed the faster recurrent convolutional neural network (RCNN) algorithm to detect pedestrian targets, and improved it to make good use of the convolutional features at different scales. Finally, support vector machine (SVM), traditional Faster RCNN, and optimized Faster RCNN algorithms were compared by simulation experiments. The results showed that the optimized Faster RCNN algorithm had higher detection accuracy and recall rate, obtained a more accurate target localization frame, and detected faster than SVM and traditional Faster RCNN algorithms; the traditional Faster RCNN algorithm had higher detection accuracy and target frame localization accuracy than the SVM algorithm.

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