System and method for detecting object in an adaptive environment using a machine learning model

A system for detecting at least one object in at least one image in a target environment that is adapted to a source environment using a machine learning model, thereby reducing a dissimilarity between a plurality of features of the target environment and the source environment, comprising:

• a memory that stores a set of instructions;
• and
• a processor that is configured to execute the set of instructions to
• extract a plurality of features (f1) from a source image associated with a source environment and a target image associated with a target environment;
• generate a feature map (ATmap1) based on the plurality of features from the source image and the target image;
• generate, using an environment classifier, a pixel-wise probability output map (D1) for the plurality of features (f1) from the source image and the target image, wherein the pixel-wise probability output map represents a probability of each pixel in the feature map belonging to the source environment;
• determine a first environment invariant feature map (SAmap1) by combining the feature map (ATmap1) with the pixel-wise probability output map (D1), wherein the environment invariant feature map represents the feature map that is invariant to any environment;
• determine a second environment invariant feature map (RSmap1) by combining the first environment invariant feature map (SAmap1) and the plurality of features (f1);
• generate a plurality of second environment invariant feature maps (RSmapn) at a plurality of instances;
• extract a plurality of environment invariant features based on the plurality of second environment invariant feature maps;
• and
• detect the at least one object in the at least one image in the target environment that is adapted to the source environment by training the machine learning model using the plurality of environment invariant features, thereby reducing a dissimilarity between the plurality of features of the target environment and the source environment.

A non-transitory computer-readable storage medium storing a sequence of instructions, which when executed by one or more processors, causes detecting at least one object in at least one image in a target environment that is adapted to a source environment using a machine learning model, thereby reducing a dissimilarity between a plurality of features of the target environment and the source environment comprising:

• extracting a plurality of features (f1) from a source image associated with a source environment and a target image associated with a target environment;
• generating a feature map (ATmap1) based on the plurality of features from the source image and the target image;
• generating, using an environment classifier, a pixel-wise probability output map (D1) for the plurality of features (f1) from the source image and the target image, wherein the pixel-wise probability output map represents a probability of each pixel in the feature map belonging to the source environment;
• determining a first environment invariant feature map (SAmap1) by combining the feature map (ATmap1) with the pixel-wise probability output map (D1), wherein the environment invariant feature map represents the feature map that is invariant to any environment;
• determining a second environment invariant feature map (RSmap1) by combining the first environment invariant feature map (SAmap1) and the plurality of features (f1);
• generating a plurality of second environment invariant feature maps (RSmapn) at a plurality of instances;
• extracting a plurality of environment invariant features based on the plurality of second environment invariant feature maps;
• and
• detecting the at least one object in the at least one image in the target environment that is adapted to the source environment by training the machine learning model using the plurality of environment invariant features, thereby reducing a dissimilarity between the plurality of features of the target environment and the source environment.