In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.
Abstruct-Simulated annealing is applied to the synthesis of arrays in order to reduce the peaks of side lobes by acting on the elements' positions and weight coefficients. In the case considered, the number of array elements and the spatial aperture of an unequally spaced array are a priori fixed. Thanks to the high flexibility of simulated annealing, the results obtained for a 25-element array over an aperture of 50X improve those reported in the literature.
Abstract-The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.
Abstract-Cognitive Radios is emerging in research laboratories as a promising wireless paradigm, which will integrate benefits of software defined radio with a complete aware communication behavior. To reach this goal many issues remain still open, such as powerful algorithms for sensing the external environment. This paper presents a further step in the direction of allowing cooperative spectrum sensing in peer-to-peer cognitive networks by using distributed detection theory. The approach aims at improving the radio awareness with respect to stand alone scenario as it is shown with theoretical and experimental results.
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 operator's 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.
We propose a multi-target tracking algorithm based on the Probability Hypothesis Density (PHD) filter and data association using graph matching. The PHD filter is used to compensate for miss-detections and to remove noise and clutter. This filter propagates the first order moment of the multi-target posterior (instead of the full posterior) to reduce the growth in complexity with the number of targets from exponential to linear. Next the filtered states are associated using graph matching. Experimental results on face, people and vehicle tracking show that the proposed multi-target tracking algorithm improves the accuracy of the tracker, especially in cluttered scenes.
In this correspondence, the achievable rates of the so called "multiple-input multiple-output interference channel," exploited by a couple of single antenna primary terminals and two antenna cognitive radios under specific interference constraints, are analyzed. In particular, by assuming perfect channel state information at the cognitive terminals, a closed form expression for a linear precoding and linear reception scheme, which guarantees to meet the achievable rates and no mutual interference between primary and cognitive terminals, is obtained. Numerical results regarding the effects of different fading channels and of an imperfect knowledge of the channel are provided to evaluate the performances of the proposed scheme in real environments.
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