Suspicious Human Activity Research Articles

Domain adaptation assisted automatic real-time human-based video summarization

The video summarization plays a momentous role for real-time surveillance for detection of suspicious human activities in public scenes. However, efficient, and accurate human-centric video summarization in real-time videos is still a challenging issue due to several inconsistencies such as noise, illumination effects, scale, or rotational variance, etc. Though, several prominent research studies are available related to human activities detection but a robust human-centric video summarization approach is a major issue. In this research, we expound an efficient video summary generation model (ReHuSum) that creates a short summary with human. Our approach employs the conception of domain adaptation for efficient summarization via transferring the knowledge of a potent pre-trained benchmark Inception-v3 model. Additionally, a frame differencing redundancy removal technique is adopted that aids in generating a precise and accurate video summary with human. Our model generates efficient short summaries from real-time videos with an overall human detection accuracy of 99.97%. Besides, we present two novel qualitative metrics namely; summarization rate (Summrate) and summarization loss (Summloss) for assessing the efficacy of overall summarization. The approach generates a real-time human-based summary of 46s with a (Summrate) of 98.7% and (Summloss) of 0.013 for an outdoor CCTV with a duration of 25.2 min. Similarly, an indoor CCTV of 40.10 min duration is summarized to 44s at a (Summrate) of 96.5% and (Summloss) of 0.035. The model can be utilized in real-time security applications (i.e., border or home surveillance, critical infrastructure, smart cities, etc.) to generate accurate video summaries with human.

Predict, Identify and Alert on Suspicious Activity by Multiple Zone

Abstract: Suspicious human activity detection in security capture is a study topic in image processing and vision. The mysterious identification of human activity from video surveillance is an area of study in both fields. Human activity can be monitored visually in conspicuous public spaces like bus depots, airports, railway stations, financial institutions, malls, schools, and universities to avoid terrorist activity, vandalism, accidents, prohibited parking spaces, vandalism, fighting chain theft, criminality, and other unusual behavior. Extremely difficult to continually monitor public spaces, thereby an innovative video surveillance installation system that can track people's movements in real-time, classify them as routine or odd, and send out an alert is needed. The field of visual surveillance to identify aberrant actions has seen a significant amount of publications in the last ten years. Furthermore There are a few surveys in the literature for recognising various abnormal activities, but none have reviewed various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the stateof-the-art in the field of recognizing suspicious behavior from surveillance recordings during the past tenyears. We provide a brief outline of the risks and challenges associated with detecting suspicious human activity. This article examines six aberrant behaviors, including the identification of abandoned objects, theft, falls, traffic accidents, and unlawful parking, as wellas the detection of violence and fire. Generally speaking, we have covered all the processes that have been [1] Foreground object extraction, object identification based on tracking or non-tracking approaches, feature extraction, classification, activity analysis, and recognition are some ofthe techniques that have been used to identify human activity from surveillance movies in the literature. This paper's goal is to give field researchers a literature assessment of six different suspicious activity identificationsystems together with its broad framework.[1]

Predict, Identify and Alert on Suspicious Activity by Multiple Zone

Abstract: An active topic of image processing and computer vision research is the detection of suspicious human behaviour in surveillance footage. In order to stop terrorism, theft,accidents, illegal parking, vandalism, fighting, chain snatching, crime, and other suspicious activities, human activity can be observed through visual surveillance in sensitive and public places like bus stations, railway stations, airports, banks, shopping malls, schools, and colleges. It is highly challenging to continually monitor public spaces, thus an intelligent video surveillance system that can track people's movements in real-time, classify them as routine or odd, and send out an alert is needed. The field of visual surveillance to identify aberrant actions has seen a significant amount of publications in the last ten years. Furthermore There are a few surveys that canbe found in the literature for the recognition of various abnormal activities, but none of them have reviewed various abnormal activities. This study presents the state-of-the-art in the field of recognising suspicious behaviour from surveillance recordings during the past tenyears. We give a quick overview of the concernsand difficulties associated in recognising suspicious human activity. This article examines six aberrant behaviours, including the identification of abandoned objects, theft, falls, traffic accidents, and unlawful parking, as wellas the detection of violence and fire. Generally speaking, we have covered all the processes that have been [1] Foreground object extraction, object identification based on tracking or non-tracking approaches, feature extraction, classification, activity analysis, and recognition are some of the techniques that have been used to identify human activity from surveillance movies in the literature. This paper's goal is to give field researchers a literature assessment of six different suspicious activity identification systems together with its broad framework.[1]

Using CNN Algorithm Detection of Suspicious Activity from Video Surveillance

Abstract: Detecting suspicious human activity involves predicting the positions of body parts or joints from an image or video. This problem has been extensively studied in computer vision for over 15 years. It is crucial because there are numerous applications that can benefit from activity detection. For instance, human pose estimation is used in various areas such as video surveillance, tracking and understanding animal behaviour, detecting sign language, improving human-computer interaction, and capturing motion without markers.

Recognition of Suspicious Human Activity in Video Surveillance: A Review

Over the past few years, there has been a noticeable growth in the use of video surveillance systems, frequently functioning as integrated systems that remotely monitor key locations. In order to prevent terrorism, theft, accidents, illegal parking, vandalism, fighting, chain snatching, and crime, human activities can be observed through visual surveillance in sensitive and public places like buses, trains, airports, banks, shopping centers, schools, and colleges. In this paper, a review of the state-of-the-art is provided, showing the overall development of identifying suspicious behavior from surveillance recordings over the past few years. We give a quick overview of the issues and difficulties associated with recognizing suspicious human activity. The purpose of this publication is to give this field's scholars a literature evaluation of several suspicious activity recognition systems along with their general structure.

Suspicious Human Activity Recognition and Alarming System

Abstract: Personal safety and social stability are seriously threatened by the violent activities. A variety of methods have been tried to curb the violent activities which includes installing of surveillance systems. It will be of great significance if the surveillance systems can automatically detect violent activities and give warning or alert signals. The whole system can be implemented with a sequence of procedures. Firstly, the system has to identify the presence of human beings in a video frame. Then, the frames which are predicted to contain violent activities has to be extracted. The irrelevant frames are to be dropped at this stage. Finally, the trained model detects violent behaviour and these frames are separately saved as images. The images along with other necessary details such as time and location is sent as an alert to the concerned authority. The proposed method is a deep learning based automatic detection approach that uses Convolutional Neural Network to detect violence present in a video. But, the disadvantage of using just CNN is that, it requires a lot of time for computation and is less accurate. Hence, a pre-trained model, Mobile Net, which provides higher accuracy and acts as a starting point for the building of the entire model. An alert message is given to the concerned authorities using telegram application.

Deep Learning Approach for Suspicious Activity Detection from Surveillance Video

Suspicious Activity is predicting the body part of a person from video. This project will entail detecting suspicious human Activity from video using neural networks. Suspicious human activity detection from surveillance video is an active research area of image processing and computer vision. Using visual surveillance, human activities can be monitored in public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, accidents and illegal parking, vandalism, fighting, crime and other suspicious activities. It is very tough to watch public places continuously, so we use an intelligent video surveillance, It is required to monitor the human activities from video and categorize them as usual and unusual activities; and can generate an alert.

DIAT-μ RadHAR (Micro-Doppler Signature Dataset) & μ RadNet (A Lightweight DCNN)—For Human Suspicious Activity Recognition

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°). A lightweight DCNN architecture ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadNet) is also designed and trained with the prepared DIAT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadHAR dataset comprising 3780 samples. The performance and recognition accuracy of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadNet is statistically computed, and the results are compared to the state-of-the-art (SOTA) CNN models. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadNet DCNN model outperforms the SOTA CNN models, giving 99.22% of overall classification accuracy, 0.09M parameters, and 0.40G floating point operations (FLOPs) with minimal false negative/positive rates. The time-complexity of the designed lightweight <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadNet DCNN model is 0.12 s, which evidences the suitability of our DCNN model for the on-device implementation.

Weapon operating pose detection and suspicious human activity classification using skeleton graphs.

Spurt upsurge in violent protest and armed conflict in populous, civil areas has upstretched momentous concern worldwide. The unrelenting strategy of the law enforcement agencies focuses on thwarting the conspicuous impact of violent events. Increased surveillance using a widespread visual network supports the state actors in maintaining vigilance. Minute, simultaneous monitoring of numerous surveillance feeds is a workforce-intensive, idiosyncratic, and otiose method. Significant advancements in Machine Learning (ML) show potential in realizing precise models to detect suspicious activities in the mob. Existing pose estimation techniques have privations in detecting weapon operation activity. The paper proposes a comprehensive, customized human activity recognition approach using human body skeleton graphs. The VGG-19 backbone extracted 6600 body coordinates from the customized dataset. The methodology categorizes human activities into eight classes experienced during violent clashes. It facilitates alarm triggers in a specific activity, i.e., stone pelting or weapon handling while walking, standing, and kneeling is considered a regular activity. The end-to-end pipeline presents a robust model for multiple human tracking, mapping a skeleton graph for each person in consecutive surveillance video frames with the improved categorization of suspicious human activities, realizing effective crowd management. LSTM-RNN Network, trained on a customized dataset superimposed with Kalman filter, attained 89.09% accuracy for real-time pose identification.

DIAT-RadHARNet: A Lightweight DCNN for Radar Based Classification of Human Suspicious Activities

Recognizing suspicious human activities is one of the critical requirements for national security considerations. Nowadays, designing the deep convolution neural network (DCNN) models suitable for micro-Doppler (m-D) signature-based human activity classification is rapidly growing. However, high computation cost and a huge number of parameters limit their direct/effective usability in field applications. This article introduces an m-D signatures’ dataset “DIAT- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> RadHAR” covering army crawling, boxing, jumping while holding a gun, army jogging, army marching, and stone-pelting/grenade-throwing, generated using an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> -band continuous wave (CW) radar. This article also introduces a lightweight DCNN model, “DIAT-RadHARNet,” designed for those human suspicious activity classification. To reduce the computation cost and to improve the generalization ability, DIAT-RadHARNet is designed with four design principles: depthwise separable convolutions, channel weighting (CHW) based on the importance, different size filters in the depthwise part, and operating different size kernels on the same input tensor. The network has 213 793 parameters with a total of 55 layers. Our extensive experimental analysis demonstrates that the DIAT-RadHARNet model efficiently classifies the activities with 99.22% accuracy, giving minimal false positive and false negative outcomes. The time complexity of the proposed DCNN model observed during the testing phase is 0.35 s. The same accuracy and time complexity are obtained even at adverse weather conditions, low-lighting environments, and long-range operations.

Thief Detection with Deep Learning using Yolo Predictive Analysis

Abstract: This paper discusses thief detection, which is one of the important applications of suspicious human activity detections. Individual safety is a major concern in our busy scheduling life. The main reason for this concern is an ever-increasing number of activities that pose a threat. A simple closed-circuit television (CCTV) installation system is not sufficient enough because it usually requires a person to be alert and monitoring the cameras always is inefficient. The necessitates for the development of a fully automated security system detects anomalous activities in real-time, and provides instant assistance to the victim. As a consequence, we proposed a framework that examines and detects suspicious human activity from real-time Surveillance video using deep learning techniques and generates an alert if abnormal activity occurs. The method was tested on a dataset with both normal and abnormal activity and yielded better results. Keywords: Thief detection, deep-learning, surveillance video, predictive analysis, yolo.

Database Creation for Normal and Suspicious Behaviour Identification in ATM Video Surveillance

A database is proposed for testing real-time activities associated with Automated Teller Machine (ATM) application. The actual videos recorded during acquisition are used for testing purposes. The scenarios for different types of occurrences in ATM cabins that can lead to the detection of suspicious human activity are documented. The work in progress involves the automated segmentation of video monitoring and database output enhancement for real- time acquisition.

Intelligent Video Surveillance using Deep Learning

Now days, Big data applications are having most of the importance and space in industry and research area. Surveillance videos are a major contribution to unstructured big data. The main objective of this paper is to give brief about video analysis using deep learning techniques in order to detect suspicious activities. Our main focus is on applications of deep learning techniques in detection the count, no of involved persons and the activity going on in a crowd considering all conditions [9]. This video analysis helps us to achieve security. Security can be defined in different terms like identification of theft, detecting violence etc. Suspicious Human Activity Detection is simply the process of detection of unusual (abnormal)l human activities . For this we need to convert the video into frames and processing these frames helps us to analyze the persons and their activities. There are two modules in this system first one Object Detection Module and Second one is Activity Detection Module .Object detection module detects whether the object is present or not. After detecting the object the next module is going to check whether the activity is suspicious or not.

Intelligent Information System for Suspicious Human Activity Detection in Day and Night

The detection of human beings in a camera attracts more attention because of its wide range of applications such as abnormal event detection, person counting in a dense crowd, person identification, fall detection for care to elderly people, etc. Over the time, various techniques have evolved to enhance the visual information. This article presents a novel 3-D intelligent information system for identifying abnormal human activity using background subtraction, rectification, morphology, neural networks and depth estimation with a thermal camera and a pair of hand held Universal Serial Bus (USB) camera to visualize un-calibrated images. The proposed system detects strongest points using Speed-Up Robust Features (SURF). The Sum of Absolute Difference (SAD) algorithm match the strongest points detected by SURF. 3-D object model and image stitching from image sequences are carried out in the proposed work. A series of images captured from different cameras are stitched into a geometrically consistent mosaic either horizontally/vertically based on the image acquisition. 3-D image and depth estimation of un-calibrated stereo images are acquired using rectification and disparity. The background is separated from the scene using threshold approach. Features are extracted using morphological operators in order to get the skeleton. Junction points and end points of the skeleton image are obtained from the skeleton. Data set of abnormal human activity is created using supervised learning such as neural network with a thermal camera and a pair of webcam. The feature vector of an activity is compared with already created data set, if a match occurs the classifier detects abnormal human activity. Additionally the proposed algorithm performs depth estimation to measure real time distance of objects dynamically. The system use thermal camera, Intel computing stick, converter, video graphics array (VGA) to high-definition multimedia interface (HDMI) and webcams. The proposed novel intelligent information system gives 94% maximum accuracy and 89% minimum accuracy for different activities, thus it effectively detects suspicious activity during day and night.

Intelligent Information System for Suspicious Human Activity Detection in Day and Night

Intelligent Information System for Suspicious Human Activity Detection in Day and Night

Suspicious human activity recognition: a review

Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different abnormal activities recognition; but none of them have addressed different abnormal activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the last decade. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This paper consists of six abnormal activities such as abandoned object detection, theft detection, fall detection, accidents and illegal parking detection on road, violence activity detection, and fire detection. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non-tracking methods, feature extraction, classification; activity analysis and recognition. The objective of this paper is to provide the literature review of six different suspicious activity recognition systems with its general framework to the researchers of this field.