Logo recognition in images and videos
Accurately detection of logos in media content on media presentation devices is addressed. Logos and products are detected in media content produced in retail deployments using a camera. Logo recognition uses saliency analysis, segmentation techniques, and stroke analysis to segment likely logo regions. Logo recognition may suitably employ feature extraction, signature representation, and logo matching. These three approaches make use of neural network based classification and optical character recognition (OCR). One method for OCR recognizes individual characters then performs string matching. Another OCR method uses segment level character recognition with N-gram matching. Synthetic image generation for training of a neural net classifier and utilizing transfer learning features of neural networks are employed to support fast addition of new logos for recognition.
1. A method to detect a particular brand in one or more images from a video stream, the method comprising:
identifying a physical location of a detected logo and associated product;
classifying the logo as being (i) on a wearable product, (ii) located on a banner, or (iii) on a fixture; and
mapping the product and a brand associated with the product to a three-dimensional (3D) map of the physical location where the logo and product were detected.
2. The method of claim 1 , wherein the physical location is an indoor or outdoor event.
3. The method of claim 1 , wherein the physical location is an indoor or outdoor retail display.
4. The method of claim 1 , further comprising:
detecting the logo based on a segmentation of one or more regions of a video frame of the video stream.
5. The method of claim 1 , further comprising:
detecting the logo in the one or more images by:
applying a saliency analysis and segmentation of one or more regions in a video frame of the video stream to determine segmented likely logo regions;
processing the segmented likely logo regions with feature matching to generate a first match, neural network classification to generate a second match, and text recognition using string matching to generate a third match;
deciding a most likely logo match based on one or more of the first match, the second match, or the third match; and
detecting the logo as the most likely logo match.
6. The method of claim 5 , wherein the segmentation comprises:
applying a stroke width transform (SWT) analysis to the one or more regions to generate SWT statistics;
applying a graph-based segmentation algorithm to establish word boxes around likely logo character strings; and
analyzing each of the word boxes to produce a set of character segmentations to delineate characters in the likely logo character strings.
7. The method of claim 1 , further comprising
segmenting the detected logo and the associated product to determine the brand.
8. A computing system comprising:
one or more processors; and
a non-transitory, computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors to perform a set of operations for detecting a particular brand in one or more images from a video stream, the set of operations comprising:
identifying a physical location of a detected logo and associated product;
classifying the logo as being (i) on a wearable product, (ii) located on a banner, or (iii) on a fixture; and
mapping the product and a brand associated with the product to a three-dimensional (3D) map of the physical location where the logo and product were detected.
9. The computing system of claim 8 , wherein the physical location is an indoor or outdoor event.
10. The computing system of claim 8 , wherein the physical location is an indoor or outdoor retail display.
11. The computing system of claim 8 , the set of operations further comprising:
detecting the logo based on a segmentation of one or more regions of a video frame of the video stream.
12. The computing system of claim 8 , the set of operations further comprising:
detecting the logo in the one or more images by:
applying a saliency analysis and segmentation of one or more regions in a video frame of the video stream to determine segmented likely logo regions;
processing the segmented likely logo regions with feature matching to generate a first match, neural network classification to generate a second match, and text recognition using string matching to generate a third match;
deciding a most likely logo match based on one or more of the first match, the second match, or the third match; and
detecting the logo as the most likely logo match.
13. The computing system of claim 12 , wherein the segmentation comprises:
applying a stroke width transform (SWT) analysis to the one or more regions to generate SWT statistics;
applying a graph-based segmentation algorithm to establish word boxes around likely logo character strings; and
analyzing each of the word boxes to produce a set of character segmentations to delineate characters in the likely logo character strings.
14. The computing system of claim 8 , the set of operations further comprising:
segmenting the detected logo and the associated product to determine the brand.
15. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a set of operations for detecting a particular brand in one or more images from a video stream, the set of operations comprising:
identifying a physical location of a detected logo and associated product;
classifying the logo as being (i) on a wearable product, (ii) located on a banner, or (iii) on a fixture; and
mapping the product and a brand associated with the product to a three-dimensional (3D) map of the physical location where the logo and product were detected.
16. The non-transitory computer-readable medium of claim 15 , wherein the physical location is an indoor or outdoor event.
17. The non-transitory computer-readable medium of claim 15 , wherein the physical location is an indoor or outdoor retail display.
18. The non-transitory computer-readable medium of claim 15 , the set of operations further comprising:
detecting the logo based on a segmentation of one or more regions of a video frame of the video stream.
19. The non-transitory computer-readable medium of claim 15 , the set of operations further comprising:
detecting the logo in the one or more images by:
applying a saliency analysis and segmentation of one or more regions in a video frame of the video stream to determine segmented likely logo regions;
processing the segmented likely logo regions with feature matching to generate a first match, neural network classification to generate a second match, and text recognition using string matching to generate a third match;
deciding a most likely logo match based on one or more of the first match, the second match, or the third match; and
detecting the logo as the most likely logo match.
20. The non-transitory computer-readable medium of claim 15 , the set of operations further comprising:
segmenting the detected logo and the associated product to determine the brand.