Abstract: A method for crowd detection in an area includes determining moving patterns of persons in the area and the number of persons within and/or moving from and/or to the area over a certain time period to obtain model training data sets; assigning each model training data set to represent one of one or more predefined crowd levels in the area; generating a crowd detection model based on the model training data sets; and estimating an actual crowd level for the area using the generated crowd detection model with actual data of moving profiles and/or the actual number of persons within and/or moving from and/or to the area over a certain time period.

Abstract: A trimmer head, comprising: a casing 21, 22, a supporting device 31, 32 for a cutting wire spool, which supporting device (31, 32) is arranged within the casing (21, 22) and movable between an engagement position, wherein it is rotatingly solid with the casing (21, 22), and a free position, wherein it is not rotatingly solid with the casing (21, 22). The head further comprises coupling means 41,42, which are so configured as to make the casing (21, 22) and the supporting device (31, 32) rotatingly solid with one another in the engagement position, engagement means (43) which are so configured as to allow displacement of the supporting device (31, 32) from the free position to the engagement position and release means (51, 52) configured for moving the supporting device (31) from the engagement position to the free position, said release means (51, 52) being activated under the effect of the centrifugal force due to rotation of the trimmer head.

Abstract: A tapping body for a head for hedge trimmer comprising a lower portion (2), destined to be tapped or pressed onto the ground, and a side portion (3) which is so structured as to be arranged through a lower opening (11) of a casing (10) of the head; a projecting edge (4), which protrudes externally to the side portion (3) and is provided with a lower waved profile (41).

Abstract: A head for edge trimmer comprising a casing (1), destined for containing at least a cutting wire reel and to be set in rotation about an axis of rotation (X), said casing exhibiting an outer surface (11) which is provided with a plurality of blind recesses (12) distributed in a pre-established manner on the outer surface (11) of the casing (1).

Abstract: A head for an edge trimmer, comprising a casing (1), destined for comprising at least a cutting wire reel and to be set in rotation about an axis of rotation (X), said casing comprising at least one outlet opening (11), which is so structured as to enable passage of a portion of the cutting wire. The head comprises two bushes (12) which laterally delimit the outlet opening (11) and are rotatable about a respective axis of rotation (Y).

Abstract: A method and computer system for detecting crowds in a location tracking system utilizes a grid and the pigeonhole principle to minimize an amount of hardware, memory and/or processing capability required in the location tracking system. In particular, an amount of storage required to determine whether a location update has resulted in a crowd grows only linearly, rather than quadratically, with the number of objects being tracked because a number of active grid points will be, at most, four times the number of objects being tracked. By determining whether an individual active grid point is within a crowd area, the computational time required for detecting a crowd within this crowd area is independent from the total number of objects being tracked.

Abstract: A method and computer system for detecting crowds in a location tracking system utilizes a grid and the pigeonhole principle to minimize an amount of hardware, memory and/or processing capability required in the location tracking system. In particular, an amount of storage required to determine whether a location update has resulted in a crowd grows only linearly, rather than quadratically, with the number of objects being tracked because a number of active grid points will be, at most, four times the number of objects being tracked. By determining whether an individual active grid point is within a crowd area, the computational time required for detecting a crowd within this crowd area is independent from the total number of objects being tracked.