Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
  • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
  • G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T5/00—Image enhancement or restoration
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T5/00—Image enhancement or restoration
  • G06T5/40—Image enhancement or restoration using histogram techniques
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/60—Analysis of geometric attributes
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/35—Categorising the entire scene, e.g. birthday party or wedding scene
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V20/00—Scenes; Scene-specific elements
  • G06V20/40—Scenes; Scene-specific elements in video content
  • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/10—Image acquisition modality
  • G06T2207/10016—Video; Image sequence
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20004—Adaptive image processing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30221—Sports video; Sports image
  • G06T2207/30228—Playing field

Definitions

• the present invention generally relates to a method and associated apparatus for analyzing video to detect far-view scenes in sports video to determine when certain image processing algorithms should be applied.
• the method comprises analyzing and classifying the fields of view of images from a video signal, creating and classifying the fields of view of sets of sequential images, and selectively applying image processing algorithms to sets of sequential images representing a particular type of field of view.
• the present invention concerns analyzing video to detect far-view scenes in sports video to determine when certain image processing algorithms should be applied.
• the method comprises analyzing and classifying the fields of view of images from a video signal, creating and classifying the fields of view of sets of sequential images, and selectively applying image processing algorithms to sets of sequential images representing a particular type of field of view.
• FIG. 1 is a diagram of a prior art video processing system with object localization and enhancement
• FIG. 2 is a diagram of a video processing system with object localization and enhancement utilizing far-view scene detection
• FIG. 3 is a flowchart of a method for classifying the field of view represented by a frame
• FIG. 4 is an illustration of the results of applying portions of a method for classifying the field of view represented by a frame
• FIG. 5 is a flowchart of a method of segmenting a video sequence into chunks
• FIG. 6 is a flowchart of a method of classifying the field of view of a chunk of a video sequence.
• FIG. 7 is a flowchart of a method of far-view scene detection utilizing the chunk-level classifications.
• the exemplifications set out herein illustrate preferred embodiments of the invention. Such exemplifications are not to be construed as limiting the scope of the invention in any manner. DESCRIPTION OF THE PREFERRED EMBODIMENTS
• the present invention provides a method and associated apparatus for analyzing video to detect far-view scenes in sports video to determine when certain image processing algorithms should be applied.
• the method comprises analyzing and classifying the fields of view of images from a video signal, creating and classifying the fields of view of sets of sequential images, and selectively applying image processing algorithms to sets of sequential images representing a particular type of field of view.
• the present invention may be implemented in signal processing hardware within a television production or transmission environment.
• the method is used to detect far-view scenes in sports video, with an exemplary application in soccer video.
• Far-view scenes are those corresponding to wide-angle camera views of the play field, wherein the objects of interest, for instance, the players and ball, are small enough to be easily degraded by video compression or not be clearly visible.
• Figure 1 illustrates a conventional image processing system 100 with object localization and enhancement applied to all portions of a video, regardless of the field of view of a particular scene.
• Input video 110 is first processed through object localization algorithms 120 to produce information 130 about detected objects, such as balls or pucks.
• object localization algorithms 120 to produce information 130 about detected objects, such as balls or pucks.
• Various techniques are known in the art for detecting such objects, such as the use of filters corresponding to characteristics of the object of interest.
• Information 130 about the identified objects is passed to the object enhancement stage 150 and to an object aware encoder 170.
• This information may include, for instance, location, size, trajectory, or a mask of a ball.
• Video frames 140 are processed at object enhancement stage 150 using the information 130 about the identified objects. For instance, a highlight color may be placed over the location of a ball or puck to allow the viewer to more easily identify its location.
• the resulting video frames 160 with enhancement applied to the detected objects are then encoded by the object-aware encoder 170, resulting in an output bitstream 180.
• the use of object information 130 by the object-aware encoder 170 may allow the encoding to be adjusted to preserve the visibility and appearance of identified objects in the far-view scenes, such as players or the ball. For instance, a lower compression ratio may be used for scenes in which the ball appears as a small object, or for particular areas of frames where the ball appears.
• object localization and enhancement are performed without regard to the type of view represented in the frames being processed.
• unnecessary processing is performed on some types of scenes, potentially resulting in wasted time, wasted processing resources, or image quality degradation.
• Figure 2 illustrates the data flow in a video processing system in accordance with the present invention.
• the input video 210 is first processed by a far-view scene detection algorithm 220, which will be described in detail below.
• the scenes 230 classified as far-view may then be processed separately from those scenes 240 classified as not being far- view. This may provide savings in time, savings of processing resources, or improvements in image quality.
• Far-view scenes 230 are sent to object localization and enhancement processing 250. This processing may include highlighting of a detected ball, illustration of a ball trajectory, or other enhancements.
• Non-far-view scenes 240 bypass the object localization and enhancement stage and are sent directly to the object-aware encoder 280.
• Object information 260 produced by the object localization and enhancement stage 250 and the enhanced far-view scenes 270 are also sent to the object-aware encoder 280, which produces an encoded output bitstream 290.
• object information 260 by the object-aware encoder 280 allows the encoding to be adjusted, for instance, to preserve the visibility and appearance of identified objects, such as soccer balls, in the far-view scenes. For instance, a lower compression ratio may be used for scenes in which the ball appears, or for particular areas of frames where the ball appears.
• the detection of far-view scenes of step 220 comprises the following stages: 1. Classification of each frame in the sequence as far-view (FV), maybe far- view (MFV) 1 or not far-view (NFV), described below with respect to Figure 3.
• FV far-view
• MV far-view
• NFV not far-view
• FIG. 3 is a flowchart of a method 300 of far-view frame classification, described with respect to an exemplary application of soccer video.
• An input frame 310 from a soccer video is processed into a binary field mask at a soccer field detection step 320.
• the soccer field pixels are identified using the knowledge that the field is generally covered in grass or grass-colored material.
• the result is a binary mask classifying all field pixels with a value of 1 and all non-field pixels, including objects in the field, with a value of 0.
• Various image processing techniques may then be used to then identify the boundaries of the playing field, ignore foreground objects, and create a solid field mask.
• all pixels within a simple bounding box encompassing all of the contiguous regions of field pixels above a certain area threshold may be included in the field mask.
• Other techniques, including the use of filters, may be used to identify the field and eliminate foreground objects from the field mask.
• "player-like" objects are identified through analysis of the foreground objects, which are connected sets of non-field pixels within the field boundary identified in step 320.
• foreground object o with constituent pixels ⁇ (x,, y,) ⁇
• Objects are considered "player-like" when the area, compactness, and aspect ratio each exceed a threshold related to known characteristics of players in a soccer video.
• the object o is considered "player-like."
• the maximum area A max and median area A med of all player-like objects are computed. As described above, the area of a particular object may be computed simply as the number of pixels comprising the object. Likewise, the area of the field y4 fie i d may be computed as the number of pixels comprising the field mask.
• the area of the field, the median area of player objects, and the maximum area of player objects are compared to thresholds related to the expected areas in a far-view scene. If (>4fjeid > 7*, «), (A me ⁇ i ⁇ T med ), and (/ max ⁇ A max ⁇ 7 max ), the frame is labeled as FV at step 360. That is, if the field area in the frame is large, the median player area is small, and the maximum player area is within an expected range, the field-of-view of the scene is wide, or far.
• the frame is classified as FV at step 380 if the criteria (A ⁇ > t ⁇ ) and (A max ⁇ t max ) are met at step 370. Stated otherwise, if the field area is above a lower threshold, but the maximum area of a player-like object is not above a minimum threshold, a reliable determination cannot be made based upon the single frame. If the frame is not labeled as FV or MFV, it is classified as NFV at step 390.
• method 300 is repeated for each frame in the sequence. Thus, a sequence of classifications of field of view, one per frame, is obtained.
• Figure 4 illustrates the field detection and player-like object detection processes of steps 320 and 330.
• the soccer field detection first results in a binary mask 420, classifying all field-colored pixels with a value of 1 and all non-field pixels, including objects in the field, with a value of 0.
• Objects on the field such as players, lines, and the ball, appear as holes in the mask since they are not the expected color of the field.
• the result of the determination of the boundaries of the soccer field is shown in 430.
• the holes in the mask from players, lines, and the ball are removed, creating a large contiguous field mask.
• Image 440 shows the detected foreground objects, the connected sets of non-field pixels within the field boundaries shown in 430. These include, for instance, binary masks of players, field lines, and the ball.
• Image 450 represents the result of the detection of player-like objects. Objects not meeting the thresholds for area, compactness, or aspect ratio, such as the field lines, have been eliminated.
• the frame-level classification of method 300 will generally produce some erroneous frame classifications.
• the video is segmented into sets of contiguous "similar-looking" frames called "chunks.”
• the process of identifying chunks is described below with respect to Figure 5.
• the field of view of each chunk is then classified as a whole based on the statistics of the classifications of its constituent frames, as described below with respect to Figure 6.
• Fig. 5 outlines a method 500 used for segmenting a video sequence 510 into a number of chunks based upon similarity of color histograms of contiguous sequences of frames.
• a color histogram consists of counts of the number of pixels in the frame within various ranges of color values. Frames from the same scene are likely to have similar histograms, as the camera will be pointed at the same objects, which generally have constant color. A change of scene will place different objects of different colors within the field of view, thereby generally changing the color histogram.
• the list of chunks C is empty.
• the starting frame number j, the first frame in the chunk under construction, is initialized to a value of 1 , the first frame in the video sequence.
• the color histogram of the / h frame is computed.
• H 1 is a 256-bin histogram of grayscale values of pixels in frame j. A smaller number of larger histogram bins may be utilized to reduce the computational intensity of histogram comparison.
• the color histogram of the / h frame serves as a basis for assembly of the chunk.
• the frames following frame j will be analyzed one at a time for similarity to frame j to determine if they should be included in the chunk that begins with frame/
• the color histogram H, of the / lh frame is computed using the same technique used at step 520. Then at step 535, the histogram difference between the ⁇ ih frame and the / h frame, d ti is computed.
• the interval [/, /-1] is added to the list of chunks at step 545.
• the current chunk is terminated at the previous frame, frame /-1, the last frame meeting the similarity threshold.
• the starting frame number j for the new chunk is set to the current value of / at step 565, and the process returns to step 520 for building of the next chunk.
• the frame / is considered similar enough to the initial frame of the chunk, j, to be added to the current chunk.
• Each chunk is represented by a pair [b e], where b is the beginning frame of the chunk and e is the ending frame.
• Fig. 6 shows the method 600 used to classify the field of view of each chunk as FV, MFV, or NFV.
• chunk classification is based on a strict form of majority voting among the labels of the frames in the chunk, mitigating labeling errors that occurred in the frame-level classification.
• each frame of the input video chunk 610 is classified as FV, MFV, or NFV.
• This frame-level classification may be performed using method 300 described above.
• the percentage of FV frames is computed for the chunk. If more than 50% of the frames in the chunk are determined to be FV at step 640, the whole chunk is classified as FV at step 650. That is, if the majority of constituent frames are far view, the chunk is considered far view. If the percentage of FV frames is not above 50%, the percentage of MFV frames in the chunk is computed at step 660. If more than 50% of frames are determined to be MFV at step 670, the chunk is classified as MFV at step 680. If neither criterion is satisfied, the chunk is classified as NFV at step 690. In an alternative embodiment, chunks may be classified as NFV if the frame count is below a certain threshold.
• Fig. 7 is a flowchart of a method 700 used in the overall far-view scene detection process.
• An input video sequence 710 is segmented into chunks at step 720, as described above with respect to Figure 5.
• each chunk is classified as FV, MFV, or NFV, as described with respect to Figure 6.
• an MFV chunk lies adjacent to an FV chunk, it is reclassified as FV. That is, if a determination regarding the field of view could not be made at step 730, the chunk will be considered far-view if it is adjacent to a far-view chunk.
• only MFV chunks adjacent to original FV chunks are reclassified and reclassification based on adjacency to other reclassified FV chunks is not allowed.
• step 750 all remaining MFV chunks as reclassified as NFV. That is, if a determination regarding the field of view of the chunk could not be made at step 620 and the chunk is not adjacent to a chunk identified as far-view, the chunk will be assumed to not be far-view.
• the process merges all FV chunks that lie adjacent to each other into larger chunks.
• an FV chunk has fewer than ⁇ /m in frames, it may be reclassified as NFV.
• ⁇ / min is chosen to be 30 frames. Thus, processing of short scenes may be avoided.
• step 780 all of the remaining FV chunks in C are classified as far- view scenes.
• the beginning and ending frames of each FV chunk indicate the boundaries 790 of the corresponding far-view scene.
• all remaining NFV chunks are classified as non-far-view scenes (after merging adjacent ones as described earlier).
• a list of far-view scenes S F v and a list of non-far- view scenes S NFV are obtained.
• Each scene is represented by a pair [b e], where b is the beginning frame of the scene and e is its end frame.
• the far- view scenes are sent to the object localization and enhancement modules that enhance objects of interest and generate object metadata for the object-aware encoder.
• the non-far-view scenes may be sent directly to the encoder to be encoded without object highlighting.
• various processing steps may be implemented separately or combined, and may be implemented in general purpose or dedicated data processing hardware or in software.
• the overall complexity of the method may be reduced by relaxing the criteria for objects in the field to be considered in decision-making. For example, instead of detecting player-like objects, all objects larger than a threshold area may be considered.
• grayscale pixel values for computing histograms during chunk segmentation
• full-color values e.g. RGB, YUV
• distance measures other than the SAD may be used for comparing histograms.
• a classifier e.g., support vector machine
• the proposed method may be applied to other sports or events with moving objects of interest.
• the method may be used to detect other types of scenes than far view for specialized processing.

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• 2010
  • 2010-07-19 KR KR1020127001591A patent/KR20120042849A/ko not_active Withdrawn
  • 2010-07-19 JP JP2012521612A patent/JP5686800B2/ja not_active Expired - Fee Related
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