A biologically plausible system for detecting saliency in video

Neuroscientists and cognitive scientists credit the dorsal and ventral pathways for the capability of detecting both still salient and motion salient objects. In this work, a framework is developed to explore potential models of still and motion saliency and is an extension of the original VENUS system. The early visual pathway is modeled by using Independent Component Analysis to learn a set of Gabor-like receptive fields similar to those found in the mammalian visual pathway. These spatial receptive fields form a set of 2D basis feature matrices, which are used to decompose complex visual stimuli into their spatial components. A still saliency map is formed by combining the outputs of convoluting the learned spatial receptive fields with the input stimuli. The dorsal pathway is primarily focused on motion-based information. In this framework, the model uses simple motion segmentation and tracking algorithms to create a statistical model of the motion and color-related information in video streams. A key feature of the human visual system is the ability to detect novelty. This framework uses a set of Gaussian distributions to model color and motion. When a unique event is detected, Gaussian distributions are created and the event is declared novel. The next time a similar event is detected the framework is able to determine that the event is not novel based on the previously created distributions. A forgetting term is also included that allows events that have not been detected for a long period of time to be forgotten

Efficient Real Time Face Tracking Operator Study and Implementation within Virtex FPGA Technology

International audienceIn this paper, we present the development of a new real time face-tracking operator. This last is based on motion detection techniques, less complex than techniques based on skin color, which are sensitive to illumination or those based on geometric standards which are efficient but expensive in computation tasks. The developed operator allows a robust face tracking with a considerable reduction in hardware resources, more particularly memory resources, which remains a powerful criterion in the construction of real time embedded systems. Moreover, the operator maps efficiently into a highly pipelined architecture, well suited for an implementation in reconfigurable technology. The developed operator is implemented on a Virtex FPGA architecture. The obtained experimental results show the effectiveness and correctness of our approach