Tracking object’s movement in real-time without human intervention via GVPP




Combining brain-power with seeing power, and you have the fastest, cheapest, most unexpected processor ever- human eye. Research labs the world over are striving to make a near-perfect electronic eye.

Generic visual perception processor (GVPP)' has been built up after ten long years of scientific effort. It can detect objects and track their movement in real-time without any human intervention. GVPP, that crunches 20 billion instructions per second (BIPS), models human perceptual process at hardware level by imitating separate temporal and spatial functions of eye-to-brain system. Processor observes its environment as stream of histograms regarding location and velocity of objects.

GVPP has been illustrated as capable of learning-in-place to solve a variety of pattern recognition problems. It boasts automatic normalization for changing object size, orientation and lighting conditions, and can work in daylight or darkness.

This electronic "eye" on a chip can now tackle most tasks which a normal human eye can. This includes driving safely, choosing ripened fruits, reading and identifying things. Sadly, although modelled on visual perception capabilities of human brain, this chip is not actually a medical marvel, poised to treatment the blind.

VISUAL PERCEPTION

Visual perception is the capability to interpret surrounding environment by processing information that is contained in visible light. Resulting perception is also called as eyesight, sight, or vision (adjectival form: visual, optical, or ocular). Different physiological components involved in vision are referred to jointly as the visual system, and are focus of much research in cognitive science, psychology, neuroscience, and molecular biology.

Visual system in humans and animals permits individuals to incorporate information from environment. Act of seeing starts when lens of eye focuses the image of its surroundings onto a light-sensitive membrane in back of the eye, termed as retina. Retina is in fact part of brain which is isolated to serve as the transducer for conversion of patterns of light into neuronal signals. Lens of eye focuses light on photoreceptive cells of the retina that detect photons of light and respond by producing neural impulses. These signals are processed in the hierarchical fashion by various parts of brain, from retina upstream to central ganglia in the brain.

GVPP was invented in 1992, by BEV founder Patric Pirim. It would be comparatively easy for CMOS chip to implement in hardware the separate contributions of temporal and spatial processing in brain. Brain-eye system uses layers of parallel-processing neurons which pass signal through series of pre-processing steps, resulting in real-time tracking of numerous moving objects within the visual scene.

NEURAL NETWORKS

Term neural network was usually used to refer to the network or circuit of biological neurons. Modern usage of term frequently refers to artificial neural networks, which are made up of artificial neurons or nodes. Hence the term has 2 distinct usages:

Biological neural networks are composed of real biological neurons which are connected or functionally related in the nervous system. In the field of neuroscience, they are frequently recognized as groups of neurons which carry out the specific physiological function in laboratory analysis.

Artificial neural networks are made up of interconnecting artificial neurons programming constructs which imitate properties of biological neurons for resolving artificial intelligence problems without creating a model of the real system. Neural network algorithms abstract away biological complexity by focusing on most significant information. Aim of artificial neural networks is fine, or human-like, predictive capability.

Chip could also confirm useful in unmanned air vehicles, miniature smart weapons, ground reconnaissance and other military applications, also in security access by using facial, iris, fingerprint, or height and gait identification.

Automotive industry

Robotics

In manufacturing, GVPP contain applications in robotics, particularly for dangerous jobs like feeding hot parts to forging presses, spraying toxic coatings on aircraft parts and cleaning up hazardous waste.

Military applications

Chip can be utilized in other significant pattern-recognition applications, like military target acquisition and fire control. Military applications comprise unmanned air vehicles, trajectory correction, automatic target detection, ground reconnaissance and surveillance.

Also to the above applications,  GVPP must be able to work as medical scanners, blood analyzers, cardiac monitoring, bank checks, bar code reading, seal and signature verification, trademark database indexing, construction of virtual reality environment models, human motion analysis, expression understanding, cloud identification, and many other fields.

Example:

If in case of driver falling asleep while driving a car. Initially, the driver will be identified by the device. Then microprocessor expresses the vision processor to look for corner points of rectangular area in which nose of driver would be expected to be positioned. Then eyes are recognized high speed movement of blinking of eyes. Histograms are use to check whether blinking duration is fast, then it finds out whether driver is falling asleep or not if in case sleeping it triggers the alarm.

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