This paper presents a tracking method to detect and track independently moving targets, attempting to traverse the railway, in monocular camera sequences. This method is capable of tracking the maximum number of pixels belonging to an object. The method starts by detecting and separating moving objects due to background subtraction and an energy vector-based clustering. Next, the method performs the step of tracking locally. Tracking starts by generating initial optical flow of all object pixels by propagating the optical flow of Harris corner points (calculated by Lucas–Kanade technique) using normal distribution. An iterative procedure, including Kalman filtering with adaptive parameters, color intensity difference-based optimization, and validation constraints, is then implemented to reach precise and robust optical flow estimation for the majority of the pixels of the tracked objects. Different experimental results are presented, evaluated, and discussed to show the effectiveness of the method of tracking objects that may move in complex and overlapping trajectories.
The presented work is conducted in the framework of the ANR-VTT PANsafer project (Towards a safer level crossing).
One of the objectives of the project is to develop a video surveillance system that will be able to detect and recognize
potential dangerous situation around level crossings. This paper addresses the problem of cameras positioning and
orientation in order to view optimally monitored scenes. In general, adjusting cameras position and orientation is
achieved experimentally and empirically by considering geometrical different configurations. This step requires a lot of
time to adjust approximately the total and common fields of view of the cameras, especially when constrained
environments, like level crossing environments, are considered. In order to simplify this task and to get more precise
cameras positioning and orientation, we propose in this paper a method that optimizes automatically the total and
common cameras fields with respect to the desired scene. Based on descriptive geometry, the method estimates the best
cameras position and orientation by optimizing surfaces of 2D domains that are obtained by projecting/intersecting the
field of view of each camera on/with horizontal and vertical planes. The proposed method is evaluated and tested to
demonstrate its effectiveness.
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