Real Time Human Detection And Tracking System

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.

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.

2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops

In this paper, we present a real time robust human detection and tracking system for video surveillance which can be used in varying environments. This system consists of human detection, human tracking and false object detection. The human detection utilizes the background subtraction to segment the blob and use codebook to classify human being from other objects. The optimal design algorithm of the codebook is proposed. The tracking is performed at two levels: human classification and individual tracking .The color histogram of human body is used as the appearance model to track individuals. In order to reduce the false alarm, the algorithms of the false object detection are also provided.

2020

The paper includes the various methods which are related to object detection and tracking in live video surveillance to detect the object like the face or can be used to detect the people, cars in a security camera. These days we can easily find that people are following social distancing due to COVID -19. This paper point towards the various methods of detecting the object (classification) and tracking (GMM tracking). This paper points toward the detection of movable objects in the live video monitoring then tracking will track the moving object. Detecting a moving object is really a very big task and it the origin of the method. Object detection is really difficult to implement which depends upon the shape size and color of the object. In this paper, we will study the background subtraction using the pixel-based method, optical flow method, color-based method gradient-based method and frame differencing. We will also study tracking methods like kernel-based method silhouette-based...

IJARCCE

People detection and tracking is one of the important research fields that have gained a lot of attention in the last few years. Although person detection and counting systems are commercially available today, there is a need for further research to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect problematic situations or unusual behavior. Automated video surveillance system addresses real-time observation of people within a busy environment leading to the description of their actions and interactions. It requires detection and tracking of people to ensure security, safety and site management. Object detection is one of the fundamental steps in automated video surveillance. Object detection from the video sequence is mainly performed by background subtraction technique. It is widely used approach for detecting moving objects from static cameras. As the name suggests, background subtraction is the process of separating out the foreground objects from the background in a sequence of video frames. The main aim of the surveillance system here is, to detect and track an object in motion by using single camera. Camera is fixed at the required place background subtraction algorithm is used for segmenting moving object in video. If human entity is detected the tracking lines are formed around human and the object is tracked. The system when realizes the human entry, it is processed in a second and the alert is produced for the security purpose. The main aim is to develop a realtime security system.

2010

Recent research in video surveillance system has shown an increasing focus on creating reliable systems utilizing non-computationally expensive technique for observing humans' appearance, movements and activities, thus providing analytical information for advanced human behavior analysis and realistic human modeling. In order for the system to function, it requires robust method for detecting and tracking human from a given input of video streams. In this thesis, a human detection technique suitable for video surveillance is presented which requires fast computations in addition of accurate results. The techniques proposed include adaptive frame differencing for background subtraction, contrast adjustment for shadow removal, and shape based approach for human detection. The tracking technique on the other hand uses correspondence approach. Event Based Video Retrieval (EBVR) system is also proposed for efficient surveillance data management and automated human recognition with unique ID assignment. Proposed human detection and tracking are integrated with EBVR and motion detection into a complete automated surveillance system called Active Vis Video Surveillance Analysis System (AVSAS) which produces good result and real-time performance especially in non-crowded scene. The EBVR system also proves to be able to handle automated human recognition with unique ID assignment accurately.

2014

Moving object detection and tracking is often the first step in applications such as video surveillance. The main aim of project is moving object/people detection and tracking system with a static camera to provide a system that tracks particular person in large number of video clips and gives us a single video clip consisting of several video clips combined together. We propose a general moving object detection and tracking based on vision system using image background subtraction algorithm. This paper focuses on detection of moving objects in a scene for example moving people talking with each other, and tracking and detection of people as long as they stay in the scene. This is done by background subtraction algorithm with the help of Simulink in MATLAB software. In this paper we estimated the position of moving people and tag them by particular Id. And then this Id is used to identify them in other videos captured by multiple camera networks.

2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), 2013

This paper focuses on algorithms which are used to count the number of people moving in or out of an area supervised by a single fixed overhead camera. The algorithms presented here have the capability of determining people count for a single person as well as for multiple people crossing the range of camera. The overall mechanism has been divided into five modules and each one of them has been explained in detail.An efficient algorithm has been proposed for tracking single as well as multiple persons in the scene with the help of tracking using the center of gravity approach. Counting is basically done by tracking the person/people in the range of camera. The proposed system, however, faces certain limitations like the background must be constant, illumination should be invariant, static object problem etc.

In the last decade, due to the increase in terrorist activities and general social problems, providing security to citizens have become the top most priorities for almost all the nations and for the same, a very close watch is required to be kept in the areas that needs security. Keeping human watch 24x7 is not possible as we all know that humans can easily be distracted and a small distraction in very sensitive and highly secure area can lead to big loses. To overcome this human flaw in the area of monitoring, the concept of making monitoring automatic came into existence. Since, video surveillance has came in the market, researches have been taking place in order to make to more easy, accurate, fast and intelligent. The goal of visual surveillance is not only to put cameras in place of human eyes, but also to accomplish the entire surveillance task as automatically as possible. In one statement we can say that video surveillance is nothing but taking the video, identifying unwanted entities, tracking their actions, understanding their actions and raising an alarm. In this paper, we will be study the phases of the video surveillance system. We will see the 3 main methods of human detection. Further, we will see most salient region method for tracking and in this paper we propose a method of handling occlusion using velocity and direction information.

People tracking are highly used in the surveillance applications. In this paper we introduced a system to track the person inoder to find the intruder and make the surveillance area more secure. Here the different aspects are taken for the input video that are conversion from color image to gray, binary and also the grayscale image and grayscale to binary are considering. And comparing the result with each other to find out the reduced time complexity of the video processing.