WO2008093321A1 - Method and system for video indexing and video synopsis - Google Patents
Method and system for video indexing and video synopsis Download PDFInfo
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- WO2008093321A1 WO2008093321A1 PCT/IL2007/001520 IL2007001520W WO2008093321A1 WO 2008093321 A1 WO2008093321 A1 WO 2008093321A1 IL 2007001520 W IL2007001520 W IL 2007001520W WO 2008093321 A1 WO2008093321 A1 WO 2008093321A1
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B27/00—Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
- G11B27/10—Indexing; Addressing; Timing or synchronising; Measuring tape travel
- G11B27/19—Indexing; Addressing; Timing or synchronising; Measuring tape travel by using information detectable on the record carrier
- G11B27/28—Indexing; Addressing; Timing or synchronising; Measuring tape travel by using information detectable on the record carrier by using information signals recorded by the same method as the main recording
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/738—Presentation of query results
- G06F16/739—Presentation of query results in form of a video summary, e.g. the video summary being a video sequence, a composite still image or having synthesized frames
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/783—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
- G06F16/7837—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content using objects detected or recognised in the video content
-
- G—PHYSICS
- G11—INFORMATION STORAGE
- G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
- G11B27/00—Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
- G11B27/02—Editing, e.g. varying the order of information signals recorded on, or reproduced from, record carriers
- G11B27/031—Electronic editing of digitised analogue information signals, e.g. audio or video signals
- G11B27/034—Electronic editing of digitised analogue information signals, e.g. audio or video signals on discs
Definitions
- Video synopsis can be used for indexing, retrieval and browsing as many objects in a covered time period are shown in a short synopsis video. However, since many different objects are shown simultaneously, examining the simple synopsis video may be confusing.
- a computer- implemented method for a computer-implemented method for generating a synopsis video from a source video comprising: selecting at least three different source objects according to one or more defined constraints, each source object being a connected subset of image points from at least three different frames of the source video; sampling from each selected source object one or more synopsis objects by temporal sampling using image points derived from specified time periods; determining for each synopsis object a respective time for starting its display in the synopsis video; and displaying the synopsis video by displaying selected synopsis objects or objects derived therefrom at their respective times; such that in the synopsis video at least three points that each derive from different respective times in the source video are displayed simultaneously.
- a system for generating a synopsis video from a source video comprising: a source object selector adapted to be coupled to an object memory that stores objects in a source video for selecting at least three different source objects according to one or more defined constraints, each source object being a connected subset of image points from at least three different frames of the source video; a synopsis object sampler coupled to the source object selector for sampling from each selected source object one or more synopsis objects by temporal sampling using image points derived from specified time periods; a time selection unit coupled to the synopsis object sampler for determining for each synopsis object a respective time for starting its display in the synopsis video; a stitching unit coupled to the time selection unit for stitching the selected synopsis objects or objects derived therefrom at their respective times so as to generate successive synopsis video frames, such that in the synopsis video frames at least three points that each derive from different respective times in the source video are displayed simultaneously; and a synopsis frame
- Figs. 12a and 12b show synopsis frames generated from a video captured overnight in St. Louis;
- E 1 (X) is the unary-color term
- E 2 (f r ,f s ) is the pairwise-contrast term between adjacent pixels r and s
- ⁇ is a user defined weight
- fuzzy objects can be multiplied by the fuzzy membership values of the involved elements. Also, when a fuzzy object is inserted into an image, the membership value can be used "alpha matting", allowing a transparency effect.
- the temporal consistency cost adds a bias towards preserving the chronological order of events.
- the preservation of chronological order is more important for tubes that have a strong interaction. For example - it would be preferred to keep relative time of two people talking to each other, or keep the chronological order of two events with a reasoning relation. Yet, it is very difficult to detect such interactions. Instead, the amount of interaction d ⁇ b,b') between each pair of tubes is estimated for their relative spatio-temporal distance as described below:
- tubes b and b' do not share a common time at the synopsis video, and assuming that b is mapped to earlier time than b' , their interaction diminishes exponentially with time:
- ⁇ tme is a parameter defining the extent of time in which events are still considered as having temporal interaction.
- C is a constant penalty for events that do not preserve temporal consistency.
- MRF-based techniques such as Belief Propagation [23] or Graph Cuts [10].
- the simulated annealing was applied in the space of all possible temporal mappings M , including the special case when a tube is not used at all in the synopsis video.
- Each state describes the subset of tubes that are included in the synopsis, and neighboring states are defined as states in which a single activity tube is removed or changes its mapping into the synopsis.
- As an initial state we used the state in which all tubes are shifted to the beginning of the synopsis movie. Also, in order to accelerate computation, it is possible to restrict the temporal shifts of tubes to be in jumps of 10 frames.
- frames corresponding to such time periods are commonly filtered out during the online phase.
- the original time of the remaining frames is recorded together with each frame.
- frames were recorded according to two criteria: (I) A global change in the scene, measured by the sum of squared difference (SSD) between the incoming frame and the last kept frame. This criterion tracked the lighting changes expressed by a gradual illumination change in the entire frame. (2) Existence of a moving object, measured by the maximal SSD in small windows.
- video activity can be measured only once in a few frames.
- the Object Queue One of the main challenges in handling endless videos is developing a scheme to
- N t K-e ' ⁇ , (13) ⁇
- K is determined to control the total number of objects in the queue.
- FIG. 6 An example of temporal distribution of objects arriving into the queue appears in Fig. 6, which shows graphically temporal distribution of activities, as measured by number of moving objects, at the airport scene of Fig. 2 over 29 hours. There are 1,920 objects during this period. Exponential decay of objects in the queue will result in an age distribution which is proportional to the arrival distribution multiplied by a decaying exponential.
- Stitching the Synopsis Video The stitching of tubes from different time periods poses a challenge to existing methods (such as [1, 16]). Stitching all the tubes at once may result in a blending of colors from different objects, which is an undesired effect. It is better to preserve the sharp transitions between different objects, while eliminating the seams only between the objects and the background. An accurate segmentation of the objects may solve this problem, but an accurate segmentation is unrealistic. Instead, the boundaries of each tube consist of background pixels due to the morphological dilation we apply when generating the activity tubes.
- depth ordering when "object tubes” are combined, where closer tubes will occlude further tubes.
- a simple "ground plane” heuristic can be used, assumes that an object whose vertical image position is lower is also closer.
- Other depth ordering methods include [29]. The frequency of object occlusion cases depends on the relative weights of the collision cost (that prevent such cases) in respect to other costs.
- Fig. 7 shows a conceptual architecture of synopsis-based hierarchical video indexing and search.
- the video into which indexing is desired has already been selected, e.g. "last hour”, "last 36 hours", etc.
- the video is first analyzed and active/important objects are extracted from the video to generate an object based representation of the video.
- a clustering method such as [30] can be used to classify each object into its corresponding class. It is important to note that the clustering process can also be used to help identifying "irregular" objects and behavior. An object that is not clustered well to any of the classes can be suspicious as being “unique” or “irregular” and can be visualized with special marking in the process of the synopsis generation which is described later.
- probabilistic clustering can be used whereby, instead of having a hard decision as to which object belongs to which class, a probability vector can be defined for each object and the different classes. This can be used in the hierarchical indexing process. For example, an object can be associated with more than one class if it fits well to those classes. It can also be used in the case where irregular activity is detected as manifested by an object that has substantially equal probabilities of belonging to different classes.
- Figs. 10a and 10b show the effect of the choice of collision cost of the density of objects in the video synopsis.
- Fig. 10a shows a frame from a 20 second synopsis of a video captured over 24 hours at Stuttgart airport.
- Fig. 10b shows that reducing the "collision penalty" in the cost function substantially increases the object density, thereby allowing more overlap between objects.
- Fig. 12 shows shape based preferences. In Fig. 12a the regular cost function was used, and the large objects (moving cars) were preferred. In Fig. 12b small, dark, objects were preferred, showing a completely different pedestrian activity.
- Fig. 11 shows a frame from a short synopsis of a video captured over 9 hours in a Billiard club. Notice the multiple players per table at the synopsis.
- the object is slowed down.
- the pixel at this time can be interpolated from neighboring pixels at neighboring frames closest in time to the selected time. Any possible interpolation method may be used.
- phase transitions by looking for background changes that correspond to beginning and ending of tubes. These transitions are important as they explain the changes in the background. Since phase transitions correspond to changes in the background, the stitching of phase transitions into the background should be given special attention. Two effects may occur in the synopsis video when phase transitions are not inserted into the background at the right time, (i) Background objects will appear and disappear with no reason, causing a flickering effect, (ii) Moving objects will disappear when they stop moving, rather than become part of the background. To minimize such effects in the video synopsis, phase transitions should be inserted into the time lapse background at a time that corresponds to their original time. System hardware
- a stitching unit 26 within the frame generator 21 stitches the selected color-transformed synopsis objects so as to generate successive synopsis video frames.
- the frames of the synopsis video are stored in a synopsis frame memory 27 for subsequent processing or display by a display unit 28 that displays the temporally shifted objects at their specified time and color transformation.
- Queue management is necessary to bridge the gap between infinite video and finite storage, and to enable fast response to user queries.
- Several methodologies have been described for determining which objects should be removed from the queue once it becomes full, but other methodologies are possible. Even a random selection of objects for removal from the queue may work fine.
- the second phase occurs after the user's query is given.
- a subset of the queue is extracted based on the period of interest, and the object tubes are arranged (by temporal shifts) to generate the optimal video synopsis.
- This stage which requires off-line computation, delivers the video synopsis to the user.
- transformation of color or grayscale is only one type of transformation that may be applied to selected synopsis object prior to stitching. As explained above, this is particularly beneficial when generating a synopsis video from a source video that spans a long duration in order to ensure background consistency. But it may be less critical when a synopsis video is derived from a source video in which the background hue is sufficiently constant during the required time window.
- the system according to the invention may be a suitably programmed computer.
- the invention contemplates a computer program being readable by a computer for executing the method of the invention.
- the invention further contemplates a machine-readable memory tangibly embodying a program of instructions executable by the machine for executing the method of the invention.
Abstract
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Priority Applications (11)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN200780050610.0A CN101689394B (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
US12/522,843 US8311277B2 (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
JP2009547808A JP5355422B2 (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
BRPI0720802-2A BRPI0720802B1 (en) | 2007-02-01 | 2007-12-09 | METHOD AND SYSTEM FOR GENERATING A VIDEO SYNOPSIS OF AN UNINTERRUPTED VIDEO FLOW SOURCE SUCH AS GENERATED BY A VIDEO SECURITY CAMERA |
KR1020097018271A KR101456652B1 (en) | 2007-02-01 | 2007-12-09 | Method and System for Video Indexing and Video Synopsis |
EP07827482A EP2119224A1 (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
CA2676632A CA2676632C (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
AU2007345938A AU2007345938B2 (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
IL199678A IL199678A (en) | 2007-02-01 | 2009-07-02 | Method and system for video indexing and video synopsis |
US12/623,217 US8949235B2 (en) | 2005-11-15 | 2009-11-20 | Methods and systems for producing a video synopsis using clustering |
US13/644,850 US8818038B2 (en) | 2007-02-01 | 2012-10-04 | Method and system for video indexing and video synopsis |
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US89869807P | 2007-02-01 | 2007-02-01 | |
US60/898,698 | 2007-02-01 | ||
US91183907P | 2007-04-13 | 2007-04-13 | |
US60/911,839 | 2007-04-13 | ||
US97158207P | 2007-09-12 | 2007-09-12 | |
US60/971,582 | 2007-09-12 |
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PCT/IL2006/001320 Continuation-In-Part WO2007057893A2 (en) | 2005-11-15 | 2006-11-15 | Method and system for producing a video synopsis |
EP17152868.0A Previously-Filed-Application EP3297272A1 (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
US12/522,843 A-371-Of-International US8311277B2 (en) | 2007-02-01 | 2007-12-09 | Method and system for video indexing and video synopsis |
US9368408A Continuation-In-Part | 2005-11-15 | 2008-10-08 | |
US12/623,217 Continuation-In-Part US8949235B2 (en) | 2005-11-15 | 2009-11-20 | Methods and systems for producing a video synopsis using clustering |
US13/644,850 Continuation US8818038B2 (en) | 2007-02-01 | 2012-10-04 | Method and system for video indexing and video synopsis |
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US (2) | US8311277B2 (en) |
EP (2) | EP2119224A1 (en) |
JP (1) | JP5355422B2 (en) |
KR (1) | KR101456652B1 (en) |
CN (1) | CN101689394B (en) |
AU (1) | AU2007345938B2 (en) |
BR (1) | BRPI0720802B1 (en) |
CA (1) | CA2676632C (en) |
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EP2119224A1 (en) | 2009-11-18 |
JP2010518673A (en) | 2010-05-27 |
AU2007345938B2 (en) | 2011-11-10 |
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BRPI0720802B1 (en) | 2021-10-19 |
EP3297272A1 (en) | 2018-03-21 |
AU2007345938A1 (en) | 2008-08-07 |
US8311277B2 (en) | 2012-11-13 |
CN101689394B (en) | 2014-03-26 |
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