Elena Stringa

Real-time video-shot detection for scene surveillance applications

In this paper, a surveillance system with automatic video-shot detection and indexing capabilities is presented. The proposed system aims at detecting the presence of abandoned objects in a guarded environment and at automatically performing online semantic video segmentation in order to facilitate the human operators task of retrieving the cause of an alarm. The former task is performed by operating image segmentation based on temporal rank-order filtering, followed by classification in order to reduce false alarms. The latter task is performed by operating temporal video segmentation when an alarm is detected. In the clips of interest, the key frame is the one depicting a person leaving a dangerous object, and is determined on the basis of a feature indicating the movement around the dangerous region. Experimental results are reported in terms of static region detection, classification, clip and key-frame detection errors versus different levels of complexity of the guarded environment, in order to establish the performance that can be expected from the system in different situations.

Content-based retrieval and real time detection from video sequences acquired by surveillance systems

A surveillance system devoted to detect abandoned objects in unattended environments is presented; image processing content based retrieval capabilities have been added to make the inspection task of operators easier. Video based surveillance systems generally employ one or more cameras connected to a set of monitors. This kind of system needs the presence of a human operator, who interprets the acquired information and controls the evolution of the events in a surveyed environment. During the last years, efforts have been made to develop systems supporting human operators in their surveillance task, in order to focus the attention of operators when unusual situations are detected. Image sequence databases are also managed by the proposed surveillance system in order to provide operators with the possibility of retrieving in a second time the interesting sequences that may contain useful information for discovering causes of an alarm. Experimental results are shown in terms of the probability of correct detection of abandoned objects and examples concerning retrieval sequences.

A Long Term Change Detection Method for Surveillance Applications

A Long Term Change Detection (CD) Method is presented by definition of a probabilistic model and the integration of two different informative sources. The model is described from a theoretical point of view and its real implementation by means of a bank of shift registers is presented. The algorithm is part of a surveillance system for unattended railway stations: results on a real image sequence confirm its validity.

Remote Detection of Abandoned Objects in unattended Railway Stations by using a DS/CDMA Video-Surveillance System

The most widely used video-based surveillance systems generally employ two or more monochromatic cameras that are connected to one or more monitors. In the case of remote video-based surveillance systems, the unattended surveyed environments are generally located quite far from the control centre, where the result of the processing is displayed to a human operator. Therefore a communication system is required in order to transmit the multimedia information acquired by the sensors, digitised and processed at a local level.

A novel camera calibration algorithm based on Kalman filter

A camera calibration method for video-surveillance applications is presented. The proposed method works on the hypothesis of a fixed TV camera and it is developed in order to minimize the human intervention during the calibration process. For the application, the proposed algorithm needs the 3D measure of only one point in the scene. Other measures are simulated by using a moving object whose geometry is known and by estimating the 3D position of the object by means of an extended Kalman filter. Experimental results show that the proposed algorithm, other than simplify the installation step of video-surveillance systems, considerably improves the accuracy of the calibration with respect to similar algorithms.

Joint video-shot and layer indexing in video-surveillance application

In this paper, a joint video-shot and layer indexing technique is presented with applications to automatic surveillance of indoor environments. A video-based surveillance system has been developed that simultaneously tracks moving objects and detects the presence of abandoned objects. Whenever an abandoned abject is detected, the system is able to determine the video-shot in which a particular object (layer) appears in the guarded environment, from the first frame in which that object enters in the scene to the frame in which the object has been left. The semantic information related on both the dangerous object and the person who left it, allows the system to perform the video-shot detection and indexing tasks. What is important in a video-shot is the information related to the dangerous object present in it. For this reason a video-shot has a two level indexing: the first one is related to the characteristics of the video-shot and the second one is related to the characteristics of a particular layer.

A Markovian approach to color image restoration based on space filling curves

A method for color image restoration based on the concept of Markov random fields and space-filling curves is presented. This work is a vectorial extension of a scalar deterministic solution for Markov random fields (MRFs). The proposed method represents an efficient alternative to the use of the vectorial deterministic solution for MRFs. The application of the space filling curve transformation allows one to apply the MRF algorithm to a scalar image with N/sup 3/ grey levels (typically N=256). The scalar MRF approach is based on expressing the energy function by means of the Euclidean norm in the vectorial space. This approach implies a high computational load. The new method involves a computational load lower than the vectorial case because the energy function is presented in the scalar space obtained after space filling curve based transformation.

A Robust Hybrid Bayesian-Morphological Approach to Multidimensional Image Filtering

In this paper a robust method for color image restoration is proposed. The proposed method is a multichannel extension of a technique (i.e. statistical morphology) proposed in literature for restoring monochromatic images and it takes into account that RGB components of a multichannel image are not completely independent. In this paper the advantages of using the proposed method with respect to the separate application of statistical morphology to each component are presented from the point of view of both noise removal properties and computational complexity.

New morphological approach to edge detection and image restoration

In this paper a new morphological method is proposed for performing edge detection and image filtering at the same time. These operations are useful for high level image processing systems performing tasks such as pattern recognition or region segmentation. In literature two main classes of methodologies are proposed: Bayesian methods, that allow one to obtain good results with the drawback of an expensive computational load, and morphological methods that involve a better computational load and less accurate results. The proposed method is based on both mathematical morphological techniques and Markov Random Field based techniques. In this paper, experimental results are shown and it is possible to conclude that the method is suitable for processing images corrupted by structured impulsive noise such as SAR images.