Landmark detection apparatus and method for intelligent system

A landmark detection method for an intelligent system performed on a processor, the method comprising using the processor to perform a process comprising:

• generating a group of N first sample blocks in a first sampling region using a first weighted sampling method according to a first degree of dispersion, and performing a first landmark detection by comparing a feature of each first sample block of the group of N first sample blocks to a feature of a landmark model, wherein the first sampling region is set to the entirety of a frame image;
• and
• generating a group of N second sample blocks in a second sampling region using a second weighted sampling method according to a second degree of dispersion, and performing a second landmark detection by comparing a feature of each second sample block of the group of second sample blocks to the feature of the landmark model, wherein the second sampling region is set to an area less than the entirety of the frame image,
• wherein each of the first degree of dispersion and the second degree of dispersion is adaptively adjusted according to an accumulative probability in a current frame image, and then applied to a next frame image.

A tangible computer-readable recording medium for storing a program for performing a landmark detection method for an intelligent system, the method comprising:

• generating a group of N first sample blocks in a first sampling region using a first weighted sampling method according to a first degree of dispersion, and performing a first landmark detection by comparing a feature of each first sample block of the group of N first sample blocks to a feature of a landmark model, wherein the first sampling region is set to the entirety of a current frame image;
• and
• generating a group of N second sample blocks in a second sampling region using a second weighted sampling method according to a second degree of dispersion, and performing a second landmark detection by comparing a feature of each second sample block of the group of second sample blocks to the feature of the landmark model, wherein the second sampling region is set to an area less than the entirety of the current frame image,
• wherein each of the first degree of dispersion and the second degree of dispersion is adaptively adjusted according to an accumulative probability in a current frame image, and then applied to a next frame image.