Journal ArticleDOI
Abnormal behavior recognition for intelligent video surveillance systems
TLDR
Different levels of an intelligent video surveillance system (IVVS) are studied in this paper, where techniques related to feature extraction and description for behavior representation are reviewed, and available datasets and metrics for performance evaluation are presented.Abstract:
Different levels of an intelligent video surveillance system (IVVS) are studied in this review.Existing approaches for abnormal behavior recognition relative to each level of an IVVS are extensively reviewed.Challenging datasets for IVVS evaluation are presented.Limitations of the abnormal behavior recognition area are discussed. With the increasing number of surveillance cameras in both indoor and outdoor locations, there is a grown demand for an intelligent system that detects abnormal events. Although human action recognition is a highly reached topic in computer vision, abnormal behavior detection is lately attracting more research attention. Indeed, several systems are proposed in order to ensure human safety. In this paper, we are interested in the study of the two main steps composing a video surveillance system which are the behavior representation and the behavior modeling. Techniques related to feature extraction and description for behavior representation are reviewed. Classification methods and frameworks for behavior modeling are also provided. Moreover, available datasets and metrics for performance evaluation are presented. Finally, examples of existing video surveillance systems used in real world are described.read more
Citations
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Journal ArticleDOI
A survey on wearable sensor modality centred human activity recognition in health care
TL;DR: This survey aims to provide a more comprehensive introduction to Sensor-based human activity recognition (HAR) in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods.
Journal ArticleDOI
A review of state-of-the-art techniques for abnormal human activity recognition
TL;DR: The proposed literature provides feature designs of abnormal human activity recognition in a video with respect to the context or application such as fall detection, Ambient Assistive Living, homeland security, surveillance or crowd analysis using RGB, depth and skeletal evidence.
Journal ArticleDOI
A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications
Preksha Pareek,Ankit Thakkar +1 more
TL;DR: This survey presents a survey of various action recognition techniques along with the HAR applications namely, content-based video summarization, human–computer interaction, education, healthcare, video surveillance, abnormal activity detection, sports, and entertainment.
Proceedings ArticleDOI
VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature Preservation
TL;DR: A key innovation of VTNFP is the body segmentation map prediction module, which provides critical information to guide image synthesis in regions where body parts and clothing intersects, and is very beneficial for preventing blurry pictures and preserving clothing and body part details.
Journal ArticleDOI
A comprehensive review on deep learning-based methods for video anomaly detection
TL;DR: This survey presents a comprehensive study of the deep learning-based methods reported in state of the art to detect the video anomalies in terms of datasets, computational infrastructure, and performance metrics for both quantitative and qualitative analyses.
References
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Proceedings ArticleDOI
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