A Systematic Human Counting at Guest House using Sensing Device Technique

The application of vision detector using sensing device techniques is important in systematic counting of people both indoors and outdoors. This technique is broadly used in auditorium, lecture theatre and public market. In this paper, the technique uses a camera attached to an Android-based mobile phone which is then applied to capture images that are then transferred to a storage system via USB for image processing and counting. Also, a model for counting people indoors and outdoors is developed. Also, accurate human counting is observe

Fade Depth Prediction Using Human Presence for Real Life WSN Deployment

Current problem in real life WSN deployment is determining fade depth in indoor propagation scenario for link power budget analysis using (fade margin parameter). Due to the fact that human presence impacts the performance of wireless networks, this paper proposes a statistical approach for shadow fading prediction using various real life parameters. Considered parameters within this paper include statistically mapped human presence and the number of people through time compared to the received signal strength. This paper proposes an empirical model fade depth prediction model derived from a comprehensive set of measured data in indoor propagation scenario. It is shown that the measured fade depth has high correlations with the number of people in non-line-of-sight condition, giving a solid foundation for the fade depth prediction model. In line-of-sight conditions this correlations is significantly lower. By using the proposed model in real life deployment scenarios of WSNs, the data loss and power consumption can be reduced by the means of intelligently planning and designing Wireless Sensor Network

Segmentation and Counting of People Through Collaborative Augmented Environment

People counting system have wide potential application including video surveillance and public resources management. Also with rapid development of economic society, crowd flowing in varies public places and facility is more and more frequent. Effectively managing and controlling crowd in public places become an important issue. People counting system based on this kind of demand arises, which can be used in commercial domain such as market survey, traffic management as well as architectural design domain. For example suppose there is a crowd gathering at specific place then it indicates an unusual situation and second one if counting of people is done in shopping mall then it provides valuable information for optimizing trading hours, as well as evaluating the attractiveness of some shopping areas

A LITERATURE STUDY ON CROWD(PEOPLE) COUNTING WITH THE HELP OF SURVEILLANCE VIDEOS

The categories of crowd counting in video falls in two broad categories: (a) ROI counting which estimates the total number of people in some regions at certain time instance (b) LOI counting which counts people who crosses a detecting line in certain time duration. The LOI counting can be developed using feature tracking techniques where the features are either tracked into trajectories and these trajectories are clustered into object tracks or based on extracting and counting crowd blobs from a temporal slice of the video. And the ROI counting can be developed using two techniques: Detection Based and Feature Based and Pixel Regression Techniques. Detection based methods detect people individually and count them. It utilizes any of the following methods:- Background Differencing, Motion and Appearance joint segmentation, Silhouette or shape matching and Standard object recognition method. Regression approaches extract the features such as foreground pixels and interest points, and vectors are formed with those features and it uses machine learning algorithms to subside the number of pedestrians or people. Some of the common features according to recent survey are edges, wavelet coefficients, and combination of large set of features. Some of the common Regressions are Linear Regression, Neural Networks, Gaussian Process Regression and Discrete Classifiers. This paper aims at presenting a decade survey on people (crowd) counting in surveillance videos

Automatic counting of interacting people by using a single uncalibrated camera

Automatic counting of people, entering or exiting a region of interest, is very important for both business and security applications. This paper introduces an automatic and robust people counting system which can count multiple people who interact in the region of interest, by using only one camera. Two-level hierarchical tracking is employed. For cases not involving merges or splits, a fast blob tracking method is used. In order to deal with interactions among people in a more thorough and reliable way, the system uses the mean shift tracking algorithm. Using the first-level blob tracker in general, and employing the mean shift tracking only in the case of merges and splits saves power and makes the system computationally efficient. The system setup parameter can be automatically learned in a new environment from a 3 to 5 minutevideo with people going in or out of the target region one at a time. With a 2GHz Pentium machine, the system runs at about 33fps on 320x240 images without code optimization. Average accuracy rates of 98.5 % and 95 % are achieved on videos with normal traffic flow and videos with many cases of merges and splits, respectively. 1