Anomaly Detection in Surveillance Video Using Pose Estimation

Thyagarajmurthy, A.; Ninad, M. G.; Rakesh, Baboo; Niranjan, S. K.; Manvi, Bharat

doi:10.1007/978-981-13-5802-9_66

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Video-based systems for recognizing gestures and activities have been extensively investigated [ 56 , 57 ]. Furthermore, this issue is particularly advantageous to security, surveillance [ 58 , 59 ], and interactive applications [ 60 , 61 ]. Vision-based HAR has continued to be the primary focus of study in recent years, since it is more cost-effective and simpler to acquire than data captured through sensors.…”

Section: Human Action Recognition Frameworkmentioning

confidence: 99%

Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

Morshed

Sultana

Alam

et al. 2023

Sensors

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Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and hand-crafted techniques. We also explain a generic architecture to recognize human actions in the real world and its current prominent research topic. At long last, we are able to offer some study analysis concepts and proposals for academics. In-depth researchers of human action recognition will find this review an effective tool.

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Section: Human Action Recognition Frameworkmentioning

confidence: 99%

Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

Morshed

Sultana

Alam

et al. 2023

Sensors

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“…The identification of gestures and actions based on video analysis has been extensively researched [23], [24]. Furthermore, this issue is particularly beneficial to video surveillance [25], [26] and interactive media [27], [28]. Because vision-based data is less expensive and easier to acquire than sensor-based data, the great amount of research has concentrated on vision-based HAR in recent years.…”

Section: Har Has Been An Active Area Of Research In Computer Vision A...mentioning

confidence: 99%

Ensembled Transfer Learning Based Multichannel Attention Networks for Human Activity Recognition in Still Images

et al. 2022

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Human activity recognition is one of the most difficult tasks in computer vision. Due to the lack of time information, detecting human activities from still photos is more difficult than sensor-based or videobased techniques. Recently, various deep learning based solutions are being proposed one after another, and their performance is constantly improving. In this paper, we proposed a convolutional neural architecture by ensembling transfer learning based multi-channel attention networks. Here, four CNN branches were used to make feature fusion based ensembling and in each branch, an attention module was used to extract the contextual information from the feature map produced by existing pre-trained models. Finally, the extracted feature maps from four branches were concatenated and fed to fully connected network to produce the final recognition output. We considered 3 different datasets, Stanford 40 actions, BU-101 and Willow human actions datasets to evaluate our system. Experimental analysis showed that the proposed ensembled convolutional architecture outperformed previous works by a noteworthy margin.

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“…Human pose or posture estimation has a variety of real-life applications, including human action recognition [ 1 , 2 ], AI-powered personal trainers [ 3 , 4 ], robotics [ 5 , 6 ], motion capture and augmented reality [ 7 , 8 ], gaming [ 9 ], video surveillance [ 10 , 11 ]. Traditionally, its purpose is to predict the positions of body joints from the input images, particularly in RGB modality.…”

Section: Introductionmentioning

confidence: 99%

“…On the basis of this well-annotated dataset, we have established three baselines based on three state-of-the-art algorithms, i.e., IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. As the posture of persons being provided, this task has great significance for the extension of application scenarios, for example, AI-powered personal trainers [ 3 , 4 ], gaming [ 9 ], video surveillance [ 10 , 11 ], robotics [ 5 , 6 ], and so on, particularly for applications with privacy protection by the thermal modality. We believe this task also has far-reaching implications in computer vision perception, analysis, and interpretation and may lead to further exploration of new detection tasks beyond identification and localization.…”

Section: Introductionmentioning

confidence: 99%

Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark

Guo

Chen

Deng

et al. 2022

Sensors

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Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.

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Anomaly Detection in Surveillance Video Using Pose Estimation

Cited by 11 publications

References 11 publications

Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

Ensembled Transfer Learning Based Multichannel Attention Networks for Human Activity Recognition in Still Images

Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark

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