Analysis of Human Behavior Based On Centroid and Treading Track

IOSR Journal of Electronics and Communication Engineering, 2014

The analysis of human body motion is an important method in which computer vision combines with bio-mechanics. This method is widely used in motion detection, motion analysis, intelligent control and many other fields. In the analysis of human body motion; the moving human body detection is important part. The moving human body is detected from the background image in video sequences. Here the new method for the moving object detection based on background subtraction is defined by establishing a reliable background updating model which uses a dynamic optimization threshold method to obtain a more complete moving object. After getting moving object to remove the noise morphological filtering is done. The noise is in form of disturbances which present in the background. For removing the effect of shadow contour projection analysis is combined with the shape analysis, so that moving human body detection is done more accurately and reliably. The Background Subtraction method is accurate, faster and fits in detecting real time environment.

proposed method runs rapidly, robustly, exactly and accurate for the concurrent detection.

IOSR Journal of Computer Engineering, 2012

In current era of digital technology visual surveillance systems are persistently in pursuance of being easier to use, versatile, inexpensive and very fast. Continuous video capturing systems are the replacement for human watch, because as we know human can be easily distracted and one mistake may lead to big disaster. So video surveillance systems make this kind of work very easier for user and it provides security and control where all time watch is required. Proposed algorithm will helpful for to detect moving object and classify it as human being and keep track of moving human. This procedure is done without getting help of any additional sensing device. In this paper proposed system can classify in three steps detection, tracking and action analysis. Detection of human being is done by combination of morphological procedure and feature extraction method. Tracking of same human and occlusion handling is done in second phase. At last phase activity analysis is done and in case of any abnormal activities, an alert should be issued.

Visual surveillance systems start with motion detection or tracking. This motion object detection method attempts to locate connected regions that define or relate the moving objects within the scene; like frame-to-frame difference, background subtraction and motion analysis, For Intelligent Video Surveillance System Using Background Subtraction Technique and its Analysis this paper have used background subtraction using dynamic threshold and mixture of Gaussian. Here three different methods are used effectively for object detection and compared their basis of performance on the accurate detection. Here the techniques frame differences, dynamic threshold based detection and mixture of Gaussian model used. After the object foreground detection, the parameters like speed, velocity motion are determined. For this, the existing methods are dependent on static background for a short interval or time. In static threshold environment, due to noise the motion pattern are distinctive and hardly tolerated, which leads to a high level false positive rates compared to the previous models. To remove the unwanted pixel, filtering and morphological process are used in dynamic threshold environment. We are using an intelligence background subtraction algorithm for temporally dynamic texture scenes using a mixture of Gaussian along with this dynamic threshold, which gives an ability of greatly rarefying color variations due to the background motions but still highlighting moving objects. This proposed method proves to be an effective background subtraction technique with dynamic threshold in a dynamic environment comparing with several competitive methods and parameters after evaluating.

An Automated Video Surveillance and monitoring system has a rich history. Traditional video surveillance systems are large in number but still retrenched in an extensive manner. The system targets at tracking an object and segregates it as Human or Non-Human entities, wherein the non-human entities would be further analysed into its respective categories which would help in subsequent analytics.The system when recognizes suspicious activity, it is captured instantly and the alarm is triggered for the security purpose. It is necessary to introduce an application that automatically formulates the images captured in order to detect precarious situations or undesirable encroached objects. Object detection is a mandatory step in automated video surveillance. Foreground extraction in harmony with background subtraction is further collaborated with the threshold image for revelation of entities. The system engages a contemporary combination of Background Modelling, Support vector machine (SVM) and a Human Detection for Surveillance (HDS) System. The HDS system assimilates a Histogram of Oriented Gradients based on a human detector which is in limelight for its performance in detecting humanistic appearances. Detailed analysis is carried out on the performance of the system on various test videos.

2011

Visual surveillance in dynamic scenes, especially for human and some objects is one of the most active research areas. An attempt has been made to this issue in this work. It has wide spectrum of promising application including human identification to detect the suspicious behavior, crowd flux statistics, and congestion analysis using multiple cameras. In this paper deals with the problem of detecting and tracking multiple moving people in a static background. Detection of foreground object is done by background subtraction. Detected objects are identified and analyzed through different blobs. Then tracking is performed by matching corresponding features of blob. An algorithm has been developed in this perspective using Angular Deviation of Center of Gravity (ADCG), which gives a satisfying result for segmentation of human object.

2016

Detecting and tracking objects in crowded areas is a challenging issue in the field of Video Surveillance System. Nowadays the increase of digital video cameras, and the availability of video storage and high performance video processing hardware, opens up conceivable outcomes for tackling many video understanding problems. Developing a real-time video understanding technique which can process the large amounts of data becomes very important. The object detection first step used in surveillance applications aims to separation of foreground objects from the background. Many algorithms proposed to solve the problem of object detection, however, it still lack of tracking multiple objects in real time. Object tracking used to find a moving object detected in motion detection stage from one frame to another in an image sequence. This paper focuses on review of various techniques used in object detection and object tracking.

2018

This paper presents an efficient moving human detection algorithm based on simplified background subtraction, and human-body decision with 1D template correlation for intelligent CCTV system. In general, moving human detection algorithm consisted of object segmentation and human decision parts. In the proposed algorithm, firstly simplified background subtraction is performed to get coarse moving object regions by using simplified background modeling and updating processing. Then noise elimination using optimized adaptive threshold is done to get refined object regions. At the final step, it distinguishes the human body objects and non-human body objects by using one-dimensional template correlation. The proposed algorithm is simulated with outdoor test videos, and simulation results show the proposed algorithm is suitable for real-time video surveillance system by over 98% of correct detection rate.

2015

Recent analysis in computer vision has more and mor e targeted on building systems for observant humans and understanding their look, activities, and behavior providing advanced interfaces for interacting with humans, and making smart models of humans for numerous functions. In o rder for any of these systems to function, they req uir methods for detecting people from a given input image or a vide o. Visual analysis of human motion is currently one f the most active research topics in computer vision. In which the moving human body discovery is the mos t critical piece of the human body movement examina tio , the design is to recognize the moving human body fr om the foundation picture in feature successions, a d for the subsequent treatment, for example, the objective or der, the human body following and conduct understan ding, its compelling location assumes an essential part. Huma n ovement investigation concerns the discovery, fo llowing and acknowledgment of individuals practic...

In this survey paper we present an approach to define the existence of moving object in the video frames and to keep the track of an object’s motion and positioning. A static camera is used to grab the video. Video is actually sequence of images which are known as frames. We can identify the object using different algorithms and tracking can be defined by using different filters. Object detection and tracking can be classified using different properties of that object like color, size, texture, optical flow, edges position, shape, distance etc. Detected object can be of various categories such as humans, vehicles, birds, moving ball and other moving objects. Object tracking is used in several applications such as video surveillance, person identification, robot vision, behavior analysis, security, traffic monitoring, image retrieval, face detection, animation etc. This survey paper basically defines a brief survey of different object detection and tracking techniques using different algorithms.