Intelligent Transport System (ITS) is a method used in traffic arrangements to make efficient road transport system. One of the ITS application is the detection and tracking of vehicle objects. In this research, Gaussian Mixture Model (GMM) method was applied for vehicle detection and Kalman Filter method was applied for object tracking. The data used are vehicles video under two different conditions. First condition is light traffic and second condition is heavy traffic. Validation of detection system is conducted using Receiver Operating Characteristic (ROC) analysis. The result of this research shows that the light traffic condition gets 100% for the precision value, 94.44% for sensitivity, 100% for specificity, and 97.22% for accuracy. While the heavy traffic condition gets 75.79% for the precision value, 88.89% for sensitivity, 70.37% for specificity, and 79.63% for accuracy. With avarage consistency of Kalman Filter for object tracking is 100%.

Vehicle detection and tracking using Gaussian Mixture Model and Kalman Filter

Digital image processing is one of the most researched fields nowadays. The ever increasing need of surveillance systems has further on made this field the point of emphasis. Surveillance systems are used for security reasons, intelligence gathering and many individual needs. Object tracking and detection is one of the main steps in these systems. Different techniques are used for this task and research is vastly done to make this system automated and to make it reliable. In this research subjective quality assessment of object detection and object tracking is discussed in detail. In the proposed system the background subtraction is done from the clean original image by using distortion of color and brightness. The subtracted image is then tracked using connected component labeling. The proposed system eliminates the shadow and provides 79% accuracy.

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Object Detection and Tracking using Background Subtraction and Connected Component Labeling

In this work we present the modular Crowd Simulation Evaluation through Composition framework (CSEC) which provides a quantitative comparison between different pedestrian and crowd simulation approaches. Evaluation is made based on the comparison of source footage against synthetic video created through novel composition techniques. The proposed framework seeks to reduce the complexity of simulation evaluation and provide a platform from which the comparison of differing simulation algorithms as well as parametric tuning can be conducted to improve simulation accuracy or providing measures of similarity between crowd simulation algorithms and source data. Through the use of features designed to mimic the Human Visual System (HVS), specific simulation properties can be evaluated relative to sample footage. Validation was performed on a number of popular crowd datasets and through comparisons of multiple pedestrian and crowd simulation algorithms.

A Human and Group Behaviour Simulation Evaluation Framework utilising Composition and Video Analysis

An automatic human tracking spotlight using image processing is explained in this paper. The spotlight is used to track a person continuously on a stage. It has 2-degrees-of-freedom and provides a tilting motion which facilitates tracking. The position of the person is detected using a camera with the help of image processing. Tracking is done using Kalman filter algorithm. The spotlight is mounted on the ceiling for illuminating the person on the stage.

Automatic human tracking theatrical spotlight

Within this chapter the emerging topic of automated risk assessment in a domestic scene is discussed, state of the art techniques are reviewed followed by developed methodologies which focus on safer human and robotic interactions with an environment. By using the risk estimation framework, the notion of a quantitative risk score is presented. Hazards within a scene are evaluated and measured using risk elements which provide a numeric representation of specific types of risk. Emphasis is given to the concept of risk as a result of interaction with an environment, specifically whether human or robotic actions in a scene can effect overall risk. To this end, techniques which simulate human or robotic behaviour with regard to risk in an environment are reviewed. Specifically the ideas of interaction and visibility are addressed defining risk in terms of areas within a scene that are visited most often and which are least visible. As with any behaviour simulation techniques, validation of their accuracy is required and a number of simulation evaluation techniques are reviewed. Finally a conclusion as to the current state of automated risk assessment is given, with a brief look at the future of the research area.

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Risk Estimation in Robotics and the Impact of Human Behaviour

In the field of motion estimation for surveillance video, various techniques have been applied. One of the common approaches is Kalman filtering technique and it is interesting to explore the extension of this technique for the prediction and estimation of motion via the image sequences. In this paper, a moving object tracking in surveillance video using Kalman filter is proposed. The typical Kalman filter is good in tracking the position of a moving object. However, when dealing with occlusion, the typical Kalman filter is not able to keep tracking and predicting the position of the occluded moving object. During occlusion, the information of moving object is not available for detection and tracking. The lacking of occlusion scene determination and prediction ability cause the existing Kalman filter fails in tracking occluded object. Besides that, in the case of tracking multiple moving objects, existing Kalman filter will experience difficulties to identify the respective objects. Therefore, in order to encounter these problems, an object tracking method using enhanced Kalman filter will be developed. The ability of tracking occluded moving object will be added to increase the efficiency during tracking. Furthermore, object recognition feature will be added too to increase the accuracy of the object tracking system.

H. T Tan

Papers

Kalman filtering based object tracking in surveillance video system