A computer-implemented method for automatic product verification and shelf product gap analysis, the computer-implemented method comprising: capturing at least one far-field image data object using a far-field imager of a multi-imager imaging engine ; identifying product label information included on a product label represented within the at least one far-field image data object , wherein the product label comprises a visual indicia , wherein the far-field imager continues to capture the far-field image data object until the visual indicia is detected, wherein the product label information comprises text information included on the product label ; attempting to decode the visual indicia ; in response to successfully decoding the visual indicia in the at least one far-field image data object, capturing a near-field image data object using a near-field imager of the multi-imager imaging engine ; identifying a product set represented within the near-field image data object ; retrieving expected product information associated with the product label by querying a datastore using product identification information decoded from the visual indicia; determining whether the product label information matches the expected product information associated with the product label by comparing the product label information and the expected product information associated with the product label ; and determining whether the product set includes at least one incorrect product by comparing a product type associated with each product in the product set and an expected product type determined from the expected product information associated with the product label . capturing at least one far-field image data object using a far-field imager of a multi-imager imaging engine ; identifying product label information included on a product label represented within the at least one far-field image data object , wherein the product label comprises a visual indicia , wherein the far-field imager continues to capture the far-field image data object until the visual indicia is detected, wherein the product label information comprises text information included on the product label ; attempting to decode the visual indicia ; in response to successfully decoding the visual indicia in the at least one far-field image data object, capturing a near-field image data object using a near-field imager of the multi-imager imaging engine ; identifying a product set represented within the near-field image data object ; retrieving expected product information associated with the product label by querying a datastore using product identification information decoded from the visual indicia; determining whether the product label information matches the expected product information associated with the product label by comparing the product label information and the expected product information associated with the product label ; and determining whether the product set includes at least one incorrect product by comparing a product type associated with each product in the product set and an expected product type determined from the expected product information associated with the product label . <EOS>