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Table Of Integrals Series And Products

Emotion and Adaptation

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Simultaneous tracking of multiple persons in real-world environments is an active research field and several approaches have been proposed, based on a variety of features and algorithms. Recently, there has been a growing interest in organizing systematic evaluations to compare the various techniques. Unfortunately, the lack of common metrics for measuring the performance of multiple object trackers still makes it hard to compare their results. In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time. These metrics have been extensively used in two large-scale international evaluations, the 2006 and 2007 CLEAR evaluations, to measure and compare the performance of multiple object trackers for a wide variety of tracking tasks. Selected performance results are presented and the advantages and drawbacks of the presented metrics are discussed based on the experience gained during the evaluations.

/pdf/evaluating-multiple-object-tracking-performance-the-clear-tm8230vs80.pdf

Evaluating multiple object tracking performance: the CLEAR MOT metrics

IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

Abnormal event detection in videos using generative adversarial nets

An image stabilization algorithm is presented that is specifically oriented toward video-surveillance applications. The proposed approach is based on a novel motion-compensation method that is an adaptation of a well-known image-stabilization algorithm for visualization in video-surveillance applications. In particular, the illustrated methods take into account the specificity of typical video-surveillance applications, where objects moving in a scene often cover a large part of an image thus causing the failure of classic image-stabilization techniques. Evaluation methods for image stabilization algorithms are discussed.

Image stabilization algorithms for video-surveillance applications

A novel integrated multicamera video-sensor (panoramic catadioptric vision tracker plus - PCVT+) is proposed for surveillance systems. In the proposed setup an omni-directional imaging device is used in conjunction with a pan, tilt, zoom (PTZ) camera leading to an innovative kind of sensor that is able to automatically track at a higher zoom level any moving object within the guarded area. In particular, the catadioptric sensor is firstly calibrated and used in order to track every single object that is moving within its 360-degree field of view. Omni-directional image portions are eventually rectified and pan, tilt and zoom parameters of the moving camera are automatically adjusted by the image processing system in order to track detected objects. A cooperative strategy was developed for the selection of the object to be tracked by the PTZ sensor in the case of multiple targets.

Dual camera intelligent sensor for high definition 360 degrees surveillance

Abnormal Event Detection in Videos using Generative Adversarial Nets

In the past few years, the development of complex surveillance systems has captured the interest of both the research and industrial worlds. Strong and challenging requirements of modern society are involved in this problem, which aims to increase safety and security in several application domains such as transport, tourism, home and bank security, military applications, etc. At the same time, fast improvements in microelectronics, telecommunications, and computer science make it necessary to consider new perspectives in this field. The main objective of this paper is to investigate, discuss, and evaluate the impact of distributed processing and new communication techniques on multimedia surveillance systems, which represent the so-called third-generation surveillance systems (3 GSSs). In particular, aspects related to the distribution of intelligence among multiple-processing and wide-bandwidth resources are discussed in detail. It is shown how distribution of intelligence can be obtained by a hierarchical architecture that partitions, in a dynamic way, the main logical processing tasks (i.e., representation, recognition, and communication) performed in a 3 GSS physical architecture made up of intelligent cameras, hubs, and central control rooms. The advantages of this solution are pointed out in terms of 1) increased flexibility and reconfigurability and 2) optimal allocation of available processing and bandwidth resources. Finally, a case study is analyzed that allows one to gain a deeper insight into a distributed surveillance system.

Lucio Marcenaro

Papers

Abnormal event detection in videos using generative adversarial nets

Image stabilization algorithms for video-surveillance applications

Dual camera intelligent sensor for high definition 360 degrees surveillance

Abnormal Event Detection in Videos using Generative Adversarial Nets

Distributed architectures and logical-task decomposition in multimedia surveillance systems