A cascaded CNN model for multiple human tracking and re-localization in complex video sequences with large displacement

With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks.

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“…This increases the likelihood of false positives. Therefore, target detection is only suitable for simple scenarios [10].…”

Section: Methodsmentioning

confidence: 99%

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

Liu

Zhang²

2022

Advances in Multimedia

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“…The annotation of single action in video segments, combined with mutual communication and action practice between students, can gradually correct the action errors in the learning process. At the same time, according to the content of the teaching video, teachers pay for the wrong actions of students [29].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

[Retracted] The Importance of Traditional Sports into College Physical Education Based on Big Data Dynamic Programming Algorithm

Zhi-bin

2021

Wireless Communications and Mobile Computing

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Physical education teaching is conducive to the cultivation of students’ lifelong sports consciousness, which can improve students’ health and enhance their physique. In order to explore the importance of traditional sports based on big data dynamic programming algorithm into college physical education, the video action recognition and segmentation technology based on big data dynamic programming algorithm is designed. The complex actions in traditional sports teaching video are divided into a series of atomic actions with single semantics. The human action results are modeled according to the relationship between complex actions and atomic actions, and the actions are completed, and the changes of students’ sports level were compared under different teaching modes. Compared with the no segment method, the average accuracy of the experimental design method increased by 2.80% and 3.50%, respectively, and the action recognition rate increased by 11.50%, 8.40%, 13.60%, 13.50%, and 13.60%, respectively. Before and after the experiment, there was a significant difference in the performance of the experimental group ( P = 0.021 < 0.05 ). The results show that the traditional sports teaching mode based on video action recognition technology of big data dynamic programming algorithm can effectively improve the teaching quality of sports teaching. This research has a certain reference value to promote the current physical education teaching reform policy.

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“…When a human performs any activity, there is some sort of bodily movement, such as movements of arms and legs. In these situations, the detection of moving objects from a sequences of frames is a challenging and crucial task, and is the first step in any video analytics system such as video surveillance [30], target detection [31], human tracking [32], and robot control [33]. The selection of salient motion frames from WVSN nodes is a crucial aspect of video processing that helps us analyse only important clips, thereby effectively minimising the execution time and improving the accuracy of the violent activity recognition system.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Hussain¹,

Muhammad²,

Ullah³

et al. 2022

Computers, Materials &Amp; Continua

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Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.

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A cascaded CNN model for multiple human tracking and re-localization in complex video sequences with large displacement

Cited by 13 publications

References 33 publications

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

[Retracted] The Importance of Traditional Sports into College Physical Education Based on Big Data Dynamic Programming Algorithm

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

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