Abstract
The neurally inspired accumulative computation (AC) method and its application to motion detection have been introduced in the past years. This paper revisits the fact that many researchers have explored the relationship between neural networks and finite state machines. Indeed, finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The article shows how to reach real-time performance after using a model described as a finite state machine. This paper introduces two steps towards that direction: (a) A simplification of the general AC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation in FPGA of such a designed AC module, as well as an 8-AC motion detector, providing promising performance results. We also offer two case studies of the use of AC motion detectors in surveillance applications, namely infrared-based people segmentation and color-based people tracking, respectively.
Highlights
Motion analysis in image sequences is a constantly growing discipline due to the great number of applications in which it plays a primordial key function
Many researchers have explored the relation between discrete-time neural networks and finite state machines, either by showing their computational equivalence or by training them to perform as finite state recognizers from example [19]
Models based on local motion detection face the correspondence problem considering that a pixel in time t + ∆t is close to the same pixel in time instant t
Summary
Motion analysis in image sequences is a constantly growing discipline due to the great number of applications in which it plays a primordial key function. Optical flow in monocular video can serve as a key for recognizing and tracking moving objects, as flow data contains richer information and in experiments can successfully track difficult sequences [1] In this sense, recently some approaches have used optical-flow processing systems to analyze motion in video sequences in real-time [2, 3]. Our research team is involved in implementing the method into real-time in order to provide efficient performance in visual surveillance applications [16,17,18] In this sense, many researchers have explored the relation between discrete-time neural networks and finite state machines, either by showing their computational equivalence or by training them to perform as finite state recognizers from example [19]. 5. and 6. are the Data and results and Conclusions sections, respectively
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