Real time tracking of multiple persons using elementary tracks

International audienc

Real Time Tracking of Multiple Persons on Colour Image Sequences

International audienc

Real-time tracking of multiple persons by Kalman filtering and face pursuit for multimedia applications

International audienceWe present an algorithm that can track multiple persons and their faces simultaneously in a video sequence, even if they are completely occluded from the camera's point of view. This algorithm is based on the detection and tracking of persons masks and their faces. Face localization uses skin detection based on color information with an adaptive thresholding. In order to handle occlusions, a Kalman filter is defined for each person that allows the prediction of the person bounding box, of the face bounding box and of its speed. In case of incomplete measurements (for instance, in case of partial occlusion), a partial Kalman filtering is done. Several results show the efficiency of this method. This algorithm allows real time processing

References

Due to an increasing demand on video surveillance systems methods for real time tracking of multiple persons have become more important. A lot of work has been put into the development, implanting and testing of algorithms for this purpose and many papers on this topic have been published. This paper takes a closer look on four different methods. Three of the algorithms try to track people by recognizing the human face with different approaches. Using certain features of the detected faces these algorithms are able to distinguish between different humans. After the separation they are able to track each individual even if it is partionally occluded. The last method tries to track people with elementary tracks, the main gain from this approach is that you don’t need any knowledge on the object you are tracking or the environment you are working on. The algorithms are briefly described and finally they are compared with each other focusing on their different strengths and weaknesses in various environments. The results of the elementary track method are the most promising but in our opinion they could be further increased b

Combining computer vision and knowledge acquisition to provide real-time activity recognition for multiple persons within immersive environments

In recent years, vision is gaining increasing importance as a method of human-computer interaction. Vision techniques are becoming popular especially within immersive environment systems, which rely on innovation and novelty, and in which the large spatial area benefits from the unintrusive operation of visual sensors. However, despite the vast amount of research on vision based analysis and immersive environments, there is a considerable gap in coupling the two fields. In particular, a vision system that can provide recognition of the activities of multiple persons within the environment in real time would be highly beneficial in providing data not just for real-virtual interaction, but also for sociology and psychology research.This thesis presents novel solutions for two important vision tasks that support the ultimate goal of performing activity recognition of multiple persons within an immersive environment. Although the work within this thesis has been tested in a specific immersive environment, namely the Advanced Visualisation and Interaction Environment, the components and frameworks can be easily carried over to other immersive systems. The first task is the real-time tracking of multiple persons as they navigate within the environment. Numerous low-level algorithms, which leverage the spatial positioning of the cameras, are combined in an innovative manner to provide a high-level, extensible framework that provides robust tracking of up to 10 persons within the immersive environment. The framework uses multiple cameras distributed over multiple computers for efficiency, and supports additional cameras for greater coverage of larger areas. The second task is that of converting the low-level feature values derived from an underlying vision system into activity classes for each person. Such a system can be used in later stages to recognize increasingly complex activities using symbolic logic. An on-line, incremental knowledge acquisition (KA) philosophy is utilised for this task, which allows the introduction of additional features and classes even during system operation. The philosophy lends itself to a robust software framework, which allows a vision programmer to add activity classification rules to the system \textit{ad infinitum}. The KA framework provides automated knowledge verification techniques and leverages the power of human cognition to provide computationally efficient yet accurate classification. The final system is able to discriminate 8 different activities performed by up to 5 persons