Active perception

Active Perception is where an agents' behaviors are selected in order to increase the information content derived from the flow of sensor data obtained by those behaviors in the environment in question. In other words, in order to understand the world we move around and explore it. We sample the world through our eyes, ears, nose, skin, and tongue as we explore and construct an understanding (Perception) of the environment on the basis of this behavior (Action) . Within the construct of Active Perception, the interpretation of sensor data (perception) is inherently inseparable from the behaviors required to capture that data - action (behaviors) and perception (interpretation of sensor data) are tightly coupled. This has been developed most comprehensively with respect to vision (Active Vision) where an agent (animal, robot, human, camera mount) changes position in order to improve the view of a specific object and/or where the agent uses movement in order to perceive the environment (e.g. for obstacle avoidance).

More formally Active Perception has been defined as follows:

"Active Perception (Active Vision specifically) is defined as a study of Modeling and Control strategies for perception. By modeling we mean models of sensors, processing modules and their interaction. We distinguish local models from global models by their extent of application in space and time. The local models represent procedures and parameters such as optical distortions of the lens, focal lens, spatial resolution, band-pass filter, etc. The global models on the other hand characterize the overall performance and make predictions on how the individual modules interact. The control strategies are formulated as a search of such sequence of steps that would minimize a loss function while one is seeking the most information. Examples are shown as the existence proof of the proposed theory on obtaining range from focus and sterolvergence on 2-0 segmentation of an image and 3-0 shape parametrization".

The theory of Optical Flow was derived from concepts of Active Perception, and while optical flow is now typically considered to be a vector representation of motion captured by a vision sensor (camera), it was originally described in terms of Active Perception. The behavior of the agent (animal, robot, human) in the world generates a flow of data over the visual sensor (camera, eye) which is sampled by the sensor and interpreted into a percept of the environment by the agent, through some computation. On the basis of this percept the agent then selects another behavior that generates more data flow. Thus Optical Flow is the data flow carried by light from the environment to the vision sensor as a result of movement in the environment.

There is a related but narrower definition of active perception that perception and action are represented within the brain as the same thing. It states that when a person sees an action done, it is internally translated into, and understood within the context of, an action that they could do. This supports the capability in people and animals of learning what to do based on what they see others doing.

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