Video Based Suspicious Human Behaviour Recognition System (original) (raw)

Automated Video Surveillance to Detect Suspicious Human Activity

2013

In recent years, with the latest technological advancements, off-the-shelf cameras became vastly available, producing a huge amount of content that can be used in various application areas. Among them, visual surveillance receives a great deal of interest nowadays. Until recently, video surveillance was mainly a concern only for military or large-scale companies. However, increasing crime rate, especially in metropolitan cities, necessitates taking better precautions in security-sensitive areas, like country borders, airports or government offices, schools and colleges. Human face and human behavioural pattern play an important role in person identification. Visual information is a key source for such identifications. The proposed system aims to identify abnormal head motions, thereby prohibiting copying. It will also identify a student moving out of his place or swapping his position with another student or looking into another student's computer . We aim to develop a system th...

Suspicious Behavior Detection of People by Monitoring Camera Was sima Aitfares GENIUS laboratory

The analytic video is a very challenging area of research in computer vision. Ensure a high level of security in a public space monitored by a surveillance camera is a difficult task in recent years. Understanding people behaviors in real time allows the surveillance systems to analyze unusual events through the video frames. In this paper, we propose a new approach for detecting suspicious behavior of moving people. We are not interested in a simple motion detection of a moving object, but we analyze the trajectory of this latter; relying on the object motion vector. Once a suspicious behavior suddenly occurs in this trajectory, we segment and track this object during its motion within the camera's field of view. Experiments with real-world images validate the efficiency ofthe proposed approach.

Suspicious Human Activity Recognition for Video Surveillance System

In this research work Suspicious Human Activity Recognition for Video Surveillance System, we detected cheating activities in examination hall. We used SURF (Speed Up Robust Features) to extract interest points, and use SURF method to match and find the corresponding features. We used some algorithms to classify the suspicious activities. We also use Viola Jones object detectors for finding the faces and labelling the activities. We also use tracking algorithms to track detectors in the input video. The proposed techniques use fast detectors and they are robust. In addition to the detectors and tracking algorithms, we used text labelling to avoid false classification, if detectors and tracking algorithms fail to track the faces.

Abnormal behavior recognition for intelligent video surveillance systems: A review

Expert Systems with Applications, 2017

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.

Real-Time Detection of Suspicious Human Movement

Crime is everywhere and it could be argued that we are in one of the most crime eras in human history. Crimes like theft, violence against people and property damage are some of the rising crimes in university. On the other hand, investigation is held to find the person who is responsible for the theft, only after the crime occurred, by using existing surveillance system. It acts as ‘post-mortem’ tools. Therefore, in this paper, an appropriate algorithm for autonomous suspicious human-movement detection from surveillance videos is proposed. First, proposed system extract human-movement information by detecting and tracking people in real-time using Gaussian Mixture Model (GMM). Morphological operations aid the detection of human-movement by eliminating noises. Features extracted from human-movement are then sent for post-processing in order to recognise whether the detected activity is suspicious or not. Examples of suspicious movements are loitering and hanging or looking around in the area of interest for longer time period. The framework used for recognising suspicious activities is called Grammar-based approach. This approach is effective in detecting suspicious activities with serial of events for a longer time period. By using recorded videos with people mimicking those suspicious activities, several experiments have been performed and results presented in this paper. The experimental results presented here demonstrate the outstanding performance and low computational complexity.

Anomaly Detection through Video Surveillance using Machine Learning

International Journal of Scientific Research in Science and Technology, 2021

Anomaly Detection is identification of suspicious human behavior using real-time CCTV video. Human Anomaly Behavior has been studied as one of the main problems of computer vision for more than 15 years. It is important because of the sheer number of applications that can benefit from activity detection. For applications such as image monitoring, object tracking and formed to oversee, sign language identification, advanced human contact, and less motion capture markers, for example, human pose estimates are used. Low-cost depth sensors have disadvantages, such as restricted indoor use, and with low resolution and noisy depth information, it is difficult to estimate human poses from deep images. The proposed system therefore plans to use neural networks to solve these problems. Suspicious identification of human activity through video surveillance is an active research area in the field of image recognition and computer vision. Human activities can be monitored by video surveillance in critical and public places, such as bus stations, train stations, airports, banks, shopping malls, schools and colleges, parking lots, highways, etc. to detect terrorism, robbery, chain snatching crimes, and other suspicious activities. It is very difficult to monitor public places continuously, so it is important to have intelligent video surveillance that can track human behavior in real time and categorize it as common and unusual, and that can generate an alarm. The experimental results show that the proposed algorithm could reliably detect the unusual events in the video.

Suspicious Activity Detection And Tracking In Surveillance Videos

Journal of emerging technologies and innovative research, 2020

Recently, the utilization of security cameras for crime prevention and early detection of emergencies worldwide has been increased. The expansion in the use of surveillance cameras has aided in crime detection, captures and crime prevention. However, in many cases, it will be recognized and resolved after the occurrence of the crime and concerning continuous surveillance, the weight on the surveillance side is overwhelming and there are situations where suspicious activity may go unnoticed. To overcome this obstacle, a surveillance system that employs Human Activity Recognition techniques which can efficiently decide if the objective individual is an ordinary individual or a suspicious individual can be deployed. It is likewise expected that establishing detection systems can act as a hindrance against crime. This paper proposes a surveillance system that utilizes YOLO and ResNet for detecting suspicious individuals and activities.

Towards Intelligent Human Behavior Detection for Video Surveillance

Advancements in Computer Vision and Image Processing, 2018

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.

INTELLIGENT VIDEO SURVEILLANCE SYSTEM A RCHITECTURE FOR ABNORMAL ACTIVITY DETECTION

Video security is becoming more and more vital today due to rapid development of hardware equipments as the number of installed cameras can confirm. This paper presents the system architecture of IVSS, an Intelligent Video Surveillance System based on IPcameras and deployed in an academic environment. In fact, Video surveillance is increasingly found in academic institutions. It is used to oversee the safety of teachers and students, as well as to protect assets from vandalism and theft. In this work the surveillance system is deployed in our university environment, it is based on a set of digital IP video cameras linked by the university IP network infrastructure. Our system architecture is based on efficient moving object detecting and tracking algorithm and a robust statistical activity recognition framework based on SVM which is used for modeling activities. The experimental results on real-time video streams show the feasibility of our system and its effectiveness in human activity recognition.

Prediction of abnormal behaviors for intelligent video surveillance systems

Computational Intelligence and …, 2007

The OBSERVER is a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept. The system acquires color images from a stationary video camera and applies state of the art algorithms to segment, track and classify moving objects. In this paper we present the behavior analysis module of the system. A novel method, called Dynamic Oriented Graph (DOG) is used to detect and predict abnormal behaviors, using real-time unsupervised learning. The DOG method characterizes observed actions by means of a structure of unidirectional connected nodes, each one defining a region in the hyperspace of attributes measured from the observed moving objects and having assigned a probability to generate an abnormal behavior. An experimental evaluation with synthetic data was held, where the DOG method outperforms the previously used N-ary Trees classifier.