Dynamic visual attention model in image sequences

A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observer?s attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the user?s interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest

BEAGLEBOARD EMBEDDED SYSTEM FOR ADAPTIVE TRAFFIC LIGHT CONTROL SYSTEM WITH CAMERA SENSOR

Traffic is one of the most important aspects in human daily life because traffic affects smoothness of capital flows, logistics, and other community activities. Without appropriate traffic light control system, possibility of traffic congestion will be very high and hinder people’s life in urban areas. Adaptive traffic light control system can be used to solve traffic congestions in an intersection because it can adaptively change the durations of green light each lane in an intersection depend on traffic density. The proposed adaptive traffic light control system prototype uses Beagleboard-xM, CCTV camera, and AVR microcontrollers. We use computer vision technique to obtain information on traffic density combining Viola-Jones method with Kalman Filter method. To calculate traffic light time of each traffic light in intersection, we use Distributed Constraint Satisfaction Problem (DCSP). From implementations and experiments results, we conclude that BeagleBoard-xM can be used as main engine of adaptive traffic light control system with 91.735% average counting rate. Lalu intas adalah salah satu aspek yang paling penting dalam kehidupan sehari-hari manusia karena lalu lintas memengaruhi kelancaran arus modal, logistik, dan kegiatan masyarakat lainnya. Tanpa sistem kontrol lampu lalu lintas yang memadai, kemungkinan kemacetan lalu lintas akan sangat tinggi dan menghambat kehidupan masyarakat di perkotaan. Sistem kontrol lampu lalu lintas adaptif dapat digunakan untuk memecahkan kemacetan lalu lintas di persimpangan karena dapat mengubah durasi lampu hijau di setiap persimpangan jalan tergantung pada kepadatan lalu lintas. Prototipe sistem kontrol lampu lalu lintas menggunakan BeagleBoard-XM, kamera CCTV, dan mikrokontroler AVR. Peneliti menggunakan teknik computer vision untuk mendapatkan informasi tentang kepadatan lalu lintas dengan menggabungkan metode Viola-Jones dan metode Filter Kalman. Untuk menghitung waktu setiap lampu lalu lintas di persimpangan, peneliti menggunakan Distributed Constraint Satisfaction Problem (DCSP). Dari hasil implementasi dan percobaan dapat disimpulkan bahwa BeagleBoard-XM dapat digunakan sebagai mesin utama sistem kontrol lampu lalu lintas adaptif dengan tingkat akurasi penghitungan rata-rata sebesar 91.735%

Towards a semi-automatic situation diagnosis system in surveillance tasks

This paper describes an ongoing project that develops a set of generic components to help humans (semi-automatic system) in surveillance and security tasks in several scenarios. These components are based in the computational model of a set of selective and Active VISual Attention mechanisms with learning capacity (AVISA) and in the superposition of an ?intelligence? layer that incorporates the knowledge of human experts in security tasks. The project described integrates the responses of these alert mechanisms in the synthesis of the three basic subtasks present in any surveillance and security activity: real-time monitoring, situation diagnosing, and action planning and control. In order to augment the diversity of environments and situations where AVISA system may be used, as well as its efficiency as support to surveillance tasks, knowledge components derived from situating cameras on mobile platforms are also developed

A proposal for local and global human activities identification

There are a number of solutions to automate the monotonous task of looking at a monitor to find suspicious behaviors in video surveillance scenarios. Detecting strange objects and intruders, or tracking people and objects, is essential for surveillance and safety in crowded environments. The present work deals with the idea of jointly modeling simple and complex behaviors to report local and global human activities in natural scenes. In order to validate our proposal we have performed some tests with some CAVIAR test cases. In this paper we show some relevant results for some study cases related to visual surveillance, namely ?speed detection?, ?position and direction analysis?, and ?possible cashpoint holdup detection?

Computational agents to model knowledge - theory, and practice in visual surveillance.

In this work the concept of computational agent is located within the methodological framework of levels and domains of description of a calculus in the context of different usual paradigms in Artificial Intelligence (symbolic, situated, connectionist, and hybrid). Emphasis in the computable aspects of agent theory is put, leaving open the possibility to the incorporation of other aspects that are still pure cognitive nomenclature without any computational counterpart of equivalent semantic richness. These ideas are currently being implemented on semi-automatic video-surveillance

Revisiting algorithmic lateral inhibition and accumulative computation

Certainly, one of the prominent ideas of Professor Mira was that it is absolutely mandatory to specify the mechanisms and/or processes underlying each task and inference mentioned in an architecture in order to make operational that architecture. The conjecture of the last fifteen years of joint research of Professor Mira and our team at University of Castilla-La Mancha has been that any bottom-up organization may be made operational using two biologically inspired methods called ?algorithmic lateral inhibition?, a generalization of lateral inhibition anatomical circuits, and ?accumulative computation?, a working memory related to the temporal evolution of the membrane potential. This paper is dedicated to the computational formulations of both methods, which have led to quite efficient solutions of problems related to motion-based computer vision

Dynamic stereoscopic selective visual attention (dssva): integrating motion and shape with depth in video segmentation

Depth inclusion as an important parameter for dynamic selective visual attention is presented in this article. The model introduced in this paper is based on two previously developed models, dynamic selective visual attention and visual stereoscopy, giving rise to the so-called dynamic stereoscopic selective visual attention method. The three models are based on the accumulative computation problem-solving method. This paper shows how software reusability enables enhancing results in vision research (video segmentation) by integrating earlier works. In this article, the first results obtained for synthetic sequences are included to show the effectiveness of the integration of motion and shape features with depth parameter in video segmentation