José Manuel Menéndez

DCT based segmentation applied to a scalable zenithal people counter

This paper faces the problem of detecting the number of customers crossing an uncontrolled access to a big store, from the information retrieved by a zenithal camera. The solution operates in real time and is scalable in two ways: allows the coverage of wide accesses (spatial scalability), and works with a level of detail enough to allow detection of carried objects (functional scalability). In order to cope with the main problems of these systems - shadows, sudden changes in global lighting, and sporadic camera motion due to vibration -, a novel DCT based segmentation is presented.

A practical algorithm to correct geometrical distortion of image acquisition cameras

In this paper a new practical and simple method for removing the distortion of image acquisition cameras is described. A pattern image with straight horizontal and vertical lines is used to automatically compute the radial and tangential distortion based upon classical models, relying on the observed apparent distortion of the lines (obtained from a Cannys edge detector and making use of the Houghs Transform). The applied model is very robust and complete. Some practical results with real images are shown.

Multidimensional comparison of shot detection algorithms

Shot change detection in video sequences mainly involves two tasks: a numeric evaluation of the degree of disparity between temporally related frames, and deciding whether a value or a series of values of that disparity is or is not a shot change. This paper focuses on the first task, and presents a methodology for comparison between different criteria to evaluate disparity between frames. The main innovation respect to other comparative studies, is that it allows a multidimensional comparative analysis based on the simultaneous consideration of several criteria. In order to overcome the problems inherent to a multidimensional approach, this method is independent of any decision process, and evaluates the performance of every criterion or set of criteria via a process that results in the obtaining of a. single numerical value, which allows an extensive study and a clear and straightforward analysis of the comparative possibilities of each technique.

VISIRE: photorealistic 3D reconstruction from video sequences

Traditionally, building 3D reconstructions of large scenarios such as a museum or historical site has been costly, time consuming and required the contribution of expert personnel. Usually the results showed an artificial look and had little interactivity. However, newly developed technologies in the areas of video analysis, camera calibration and texture fusion allow us to think in a much more satisfying scenario where the user with the only aid of a domestic video camera is able to acquire all the information it is required to construct the 3D model of the desired environment in an easy and comfortable manner. In this paper, the results obtained in the EC funded project VISIRE are presented. VISIRE attempts to construct photorealistic 3D models of large scenarios using as input multiple freehand video sequences. Once acquired, the computer vision software processes the video information off-line in order to obtain the 3D mesh together with the textures required to obtain a 3D model highly resembling the original.

Real time temporal segmentation of MPEG video

One of the main customers of video annotation applications are TV broadcasters, and one of their key requirements is on-line annotation of received digital channels in order to offer dynamic value-added content-based services. For this purpose, apart from high levels of success in video temporal segmentation - a highly resource-consuming task previous to video annotation - a faster than real-time performance is also crucial. The paper describes a video temporal segmentation module able to detect abrupt transitions and all types of gradual transitions in real time, with levels of recall and precision not previously reached for so large and varied a set of video sequences.

Optimal camera exposure for video surveillance systems by predictive control of shutter speed, aperture, and gain

This paper establishes a real-time auto-exposure method to guarantee that surveillance cameras in uncontrolled light conditions take advantage of their whole dynamic range while provide neither under nor overexposed images. State-of-the-art auto-exposure methods base their control on the brightness of the image measured in a limited region where the foreground objects are mostly located. Unlike these methods, the proposed algorithm establishes a set of indicators based on the image histogram that defines its shape and position. Furthermore, the location of the objects to be inspected is likely unknown in surveillance applications. Thus, the whole image is monitored in this approach. To control the camera settings, we defined a parameters function (Ef ) that linearly depends on the shutter speed and the electronic gain; and is inversely proportional to the square of the lens aperture diameter. When the current acquired image is not overexposed, our algorithm computes the value of Ef that would move the histogram to the maximum value that does not overexpose the capture. When the current acquired image is overexposed, it computes the value of Ef that would move the histogram to a value that does not underexpose the capture and remains close to the overexposed region. If the image is under and overexposed, the whole dynamic range of the camera is therefore used, and a default value of the Ef that does not overexpose the capture is selected. This decision follows the idea that to get underexposed images is better than to get overexposed ones, because the noise produced in the lower regions of the histogram can be removed in a post-processing step while the saturated pixels of the higher regions cannot be recovered. The proposed algorithm was tested in a video surveillance camera placed at an outdoor parking lot surrounded by buildings and trees which produce moving shadows in the ground. During the daytime of seven days, the algorithm was running alternatively together with a representative auto-exposure algorithm in the recent literature. Besides the sunrises and the nightfalls, multiple weather conditions occurred which produced light changes in the scene: sunny hours that produced sharpen shadows and highlights; cloud coverages that softened the shadows; and cloudy and rainy hours that dimmed the scene. Several indicators were used to measure the performance of the algorithms. They provided the objective quality as regards: the time that the algorithms recover from an under or over exposure, the brightness stability, and the change related to the optimal exposure. The results demonstrated that our algorithm reacts faster to all the light changes than the selected state-of-the-art algorithm. It is also capable of acquiring well exposed images and maintaining the brightness stable during more time. Summing up the results, we concluded that the proposed algorithm provides a fast and stable auto-exposure method that maintains an optimal exposure for video surveillance applications. Future work will involve the evaluation of this algorithm in robotics.

Dynamic object segmentation for outdoor analysis

A new technique for dynamic object segmentation in outdoor environments is presented. It is based on an adaptive thresholding pixel-oriented strategy applied on a combination of static and dynamic differences that segments the motion presented in each image. A detection and tracking scheme helps reducing the processing areas to those in which the moving objects are present. Therefore, computational cost is highly reduced without any loss in the accuracy of the algorithm. Besides, shadows and occlusions (due to the perspective that is used) are detected and eliminated keeping similar computational savings. Results are shown on road traffic monitoring sequences.

Multiresolution image segmentation for region-based motion estimation and compensation

An intra-frame segmentation strategy to assist region-based motion estimation and compensation is presented. It is based on the multiresolution application of a histogram clustering and a probabilistic relaxation labelling algorithm, followed by a focal gradient-based bottom-up merging procedure. Specially suited for region-based video coding, it strongly differs from other proposals in that it generates arbitrary shaped image regions with pixel accuracy at a low computational cost, while allowing full reconstruction of the segmentation at the decoder without the transmission of any region description information.

Region-based multivector motion estimation for efficient very low bitrate video coding

A new multivector motion estimation and compensation strategy particularly suitable for region-based coding strategies is introduced. Region motion is described through a variable number of motion vectors applied to specific region control points. Motion estimation is carried out applying locally translational models to the control points, while more complex region motion models are used for compensation. No information about this control points is required to be transmitted,d as their determination is based on information available at the decoder. The application of this strategy within the context of a region-based hybrid video codec operating on arbitrary shaped regions where efficiency is improved eliminating the transmission of any image regions description is presented. Results showing very good quality images at transmission bit rates in the order of 40 kb/s are presented.

Motion parametric modeling for very low bitrate video coding

A camera motion compensation procedure through the use of parametric modeling is introduced. The global movement of the camera is estimated and compensated stemming from the analysis of the sequence vector field. This modeling scheme is applied to a segmentation-based hybrid video codec, allowing the system to keep bit-rates which are similar in the transmission of video sequences acquired by both static and dynamic cameras.