KR20050014559A - A detecting method and a training method of event for soccer video - Google Patents

Abstract

PURPOSE: A method of learning and detecting an event in a soccer moving picture is provided to make a scene where the event arises learned properly to the real circumstances of a national broadcast and automatically detected, by applying a hidden Markov model to soccer moving picture multimedia. CONSTITUTION: It is determined whether an event pattern of a soccer moving picture has to be learned or not(S210). Each of scenes is changed into a symbol when the event pattern is learned(S220). An observation sequence is determined from the changed symbol(S230). An event generation pattern is learned by a hidden Markov model based on the determined observation sequence(S240).

Description

Event learning and detection method of soccer video {A DETECTING METHOD AND A TRAINING METHOD OF EVENT FOR SOCCER VIDEO}

The present invention is to search for and output a specific event from the video multimedia, in particular, by applying the hidden Markov model (HIDDEN MARKOV MODEL) to the soccer video multimedia to learn the scene where the event occurs in accordance with the domestic broadcast situation and automatically search output An event learning and detection method of a soccer video.

Due to the development of computer related technology, rapid performance improvement, network speed, and mass storage device development, it is possible to use multimedia information of video that can be easily recognized and communicated quickly. Increasingly, the importance of record keeping is being confirmed.

The multimedia signal is efficiently processed and managed as a digital signal, and since the data capacity is very large, a large storage device (STORAGE DEVICE) is required. DIGITAL VIDEO LIBRARY system technology is being developed, and VOD (VIDEO ON DEMAND) services are provided by the above-described DVL technology.

The multimedia contents (CONTENTS) information is recorded and stored in a mass storage device, and, if necessary, indexed to quickly, easily and accurately retrieve desired information from the mass storage device in which the multimedia information is recorded as described above. INDEXING) to store and manage. For indexing as described above, a browsing tool or a summary file should be created.

However, in order for a person to work on such indexing directly, there is a problem that requires a lot of labor and is expensive, but it is not objective and does not obtain accurate results.

In particular, it is necessary to develop a technology for automatically searching for a desired scene or shot by video analysis, low level analysis, intermediate analysis, and advanced analysis, and quickly detecting and outputting necessary video multimedia information.

Hereinafter, an event detection method of a soccer video according to the prior art will be described with reference to the accompanying drawings.

For the purpose of illustrating the prior art, Figure 1 is a functional block diagram of an event detection apparatus of a general soccer video.

Referring to FIG. 1, the multimedia signal of the soccer video output from the video unit 10 is applied to the video analysis unit 20, and is segmented into scene information of low and intermediate levels. Each event is classified by classifying each type of scene information of the level.

As described above, each piece of information analyzed and output as low and intermediate level information and analyzed as high level information is output to the DL unit 30 and stored.

The browsing tool unit 40 connected to the DVL DIGITAL VIDEO LIBRARY unit 30 analyzes the stored event information and applies a control signal to the detection unit 50 to easily detect a desired content CONTENTS. The detection unit 50 detects and outputs a desired event from the information of the DL unit 30 according to a control signal applied from the browsing tool unit 40.

As described above, the conventional video multimedia information analysis technology includes, for example, mainly low and intermediate level video information such as long distance video information, short distance video information, replay video information, almost fixed background region video information, and the like. Will be automatically extracted.

For example, in the case of video multimedia information of a soccer sports game, a technique for searching and extracting a long distance shot of a stadium, a close shot, a replay scene, a scene with almost no movement, etc. becomes a low or intermediate level technology. .

Image information used for indexing by analyzing the video signal at a low level may include information such as color information, object motion, and edge histogram.

By using the low level analysis information as described above, middle level video information such as enlargement and reduction of the screen, field position, camera shooting target, playback speed, and use of graphic information can be extracted.

In the analyzed video multimedia, for example, there is a domain (DOMAIN) divided into a general interest-oriented video, a recording video, a war video, a sports video, and the like. In order to analyze each domain (DOMAIN), domain knowledge ( DOMAIN KNOWLEDGE), using the domain knowledge to model the corresponding content, search each event scene at an advanced level, and highlight important events by gradual scene change by the Wipe or Dissolve method. do.

The indexing method for analyzing the video signal is largely divided into a statistical method and a slice method, and the statistical method includes a difference between the current video frame and the previous video frame. By detecting the color histogram, motion (MOTION) or edge (EDGE) on a frame-by-frame basis, the area where the amount of change is prominent is indexed to the boundary point of the scene to find it.

The statistical method as described above has a problem in that it is difficult to find a scene boundary for indexing from a frame of a scene having a high momentum while being performed at a predetermined place such as a sports video signal.

In order to easily find a scene boundary for indexing from the sports video signal as described above, dividing the image frame into slices, and finding a boundary point of the scene by using the cohesion difference of the divided slices. , MS DREW proposed by VIDEO DISSOLVE AND WIPE DETECTION VIA SPATIO-TEMPORAL IMAGE OF CHROMATIC HISTOGRAM DIFFERENCES, PROC OF IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 3, PP 929-932, 2000, DETECTION OF GRADUAL TRANSITIONS THROUGH TEMPORAL SLICE ANALYSIS, PROC OF IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR'99), PP 36-41, 1999.

Since the slice method is not focused on the change of the whole frame and is not sensitive to the partial change, it shows a good performance for analyzing a video signal such as soccer.

In particular, high-level video analysis in sports video multimedia such as soccer is called EVENT analysis, which includes, for example, goals, shooting, corners, free kicks, penalty kicks, fouls, and the like.

As the information used for indexing by detecting the event (EVENT) as described above, caption information (CLOSED CAPTION), blurring phenomenon, ball (BALL) and players and goal post (GOAL POST) tracking (TRACKING) There are four types of state transition diagrams.

The caption information is provided by T. KITAHASHI in September 2000, EXTRACTINGACTORS, ACTIONS AND EVENTS FROM SPORTS VIDEO-A FUNDAMENTAL SPPROACH TO STORY TRACKING, PROC OF IEEE INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION , As suggested by BARCELONA, SPAIN, SEPT, 2000, uses caption information, which is an additional medium for multimedia videos. For example, in the case of American football, it is a method of detecting the location of an event by detecting the word TOUCH DOWN. .

The blurring phenomenon information is provided by shoe X. XU in 2002, CONTENT-BASED ANALYSIS AND INDEXING OF SPORTS VIDEO, PROC OF SPIE CONFERENCE ON STORAGE AND RETRIVAL FOR MEDIA DATABASE, SAN JOSE, USA, JAN 21-23, 2002 Proposed by, to detect the blurring that occurs because the video screen suddenly tries to follow the fast ball (BALL) when shooting occurs, it is to detect the shooting (SOOTING) and long pass (LONG PASS).

In the blurring phenomenon, when the edge EDGE is found using the MARR-HILDRETHS 'ZERO CROSSING algorithm, the amount of edges of the frame in which the blurring phenomenon occurs is significantly reduced.

In addition, tracking and using the ball, the players, and the goalpost as information detects various events such as a goal when a ball passes the goal, AF BOBICK in 1995 and 1995. Proposed by R. B. LIU and RJ QIAN in 2001, the OBJECT SEGMENTATION that separates the background from the players finds the movements of the players and traces them into a continuous mosaic of the background (MOSAIC). TRAJECTORY to track the movement of each player on the playing field.

The last state transition diagram is proposed by R. MIGLIORATI in SEMANTIC INDEXING OF MULTIMEDIA DOCUMENTS, IEEE MULTIMEDIA, VOL 9, NO 2, PP 44-51, APR-JUN, 2002 in 2002. An algorithm that detects goals, corners and free kicks using events that occur in a regular pattern.

In other words, if one team attacks the other team's goal before the goal is scored, the camera will panning or zooming in a fast motion and will shoot in the next order. To catch the scene where (BALL) enters the GOAL POST, when the CAMERA is not moving and the goal is scored, it shows the shot of the player who shot or shot the shot from a different angle of the camera. UP), a shot cut occurs.

The state transition diagram is used to find out that the goal occurs in the above sequence.

However, by detecting the above-described prior art events, the first use of caption information is different from football, in the current situation in Korea, when each broadcasting station relays or broadcasts soccer caption information. Since it does not provide, there is a fundamental problem that cannot be used.

The second method of using the blurring phenomenon is that the two events can be distinguished from each other because they use the characteristics of the screen when shooting and long pass are issued, but there is a problem that other events cannot be distinguished.

The third method of tracking the ball, the players and the goal, consumes too much load to create an IMAGE mosaic (MOSAIC) to find object separation and trajectory, There is a limiting problem of detecting only events that occur in the far scene.

In addition, the fact that the players overlap each other and that the ball is too small to be detected and obscured by the players makes it difficult to separate objects, while soccer video multimedia with a lot of camera movement can be used for moving backgrounds and foregrounds. There is a big problem that object separation is relatively difficult.

The fourth, state transition diagram method is an advanced level of information analysis method. It is assumed that a regular pattern (PATTERN) occurs when an event occurs by using a state transition graph. Detect and output.

However, it is a characteristic of sports video multimedia that various exceptions occur even in the pattern of one event EVENT, and from the soccer video in which the irregular pattern as described above occurs, using the prior art, in particular, Detecting each event by football video analysis by level, there is a problem that does not get a good result.

The present invention classifies a scene type from a soccer video multimedia, solves an exception phenomenon by statistical learning and modeling in a temporal flow using a hidden markov model, and absorbs noise and deformation. The purpose of the present invention is to automatically detect an event and provide an event learning and detection method of a soccer video suitable for the Korean broadcast situation.

The present invention devised to achieve the above object, Learning 1 process for determining whether to learn the event pattern of the soccer video; A learning 2 process of converting each scene into a symbol when the event is learned in the learning 1 process; A learning 3 process of determining an observation sequence from the symbols transformed in the learning 2 process; A learning 4 process of learning the hidden Markov model is performed using the observation sequence determined in the learning 3 process.

In addition, the present invention devised to achieve the above object, the detection 1 process of determining whether to detect each event of the soccer video; A second detection step of converting each scene into a symbol when the event is detected in the first detection step; A detection step 3 for determining an observation sequence from the symbols transformed in the detection step 2; The detection sequence is determined by using the observation sequence determined in the detection process 3 and detects the coupling probability from the hidden Markov model learning process.

In addition, the present invention devised to achieve the above object is characterized in that it detects the important highlight event of the video football game and transmits through wired and wireless television broadcasting and multimedia wireless communication equipment.

1 is a functional block diagram of an event detection apparatus of a general soccer video;

2 is a flow chart of the soccer video event learning method according to the present invention;

3 is a flowchart illustrating a method for detecting a soccer video event according to the present invention;

4 is a symbol classification state diagram according to the degree of expansion and contraction of the soccer video screen;

5 is a state diagram of symbol classification according to field positions of the present invention soccer video screen;

6 is a low level information state diagram by a camera photographing target of a soccer video;

7 is a diagram showing the analysis results of the 2002 FIFA World Cup and 2002 Busan Asian Games.

8 is a 252 symbol scene shape distribution diagram of the soccer video of the present invention;

9 is a pattern analysis diagram according to each sequence of the soccer video of the present invention;

10 is a diagram showing an example of a representative A, B, C pattern of the present invention soccer video,

11 is a distribution chart of each event occurrence pattern bar chart of the present invention soccer video;

12 is a state diagram of an event detection system of the present invention soccer video;

Fig. 13 is a diagram showing 84 symbolic analysis values of the present invention soccer video.

14 is a view showing 108 symbolic analysis values of a soccer video of the present invention;

Fig. 15 is a diagram showing the distribution of 84 symbols and 108 symbols of a soccer video.

FIG. 16 is a diagram illustrating a shooting event detection frequency analysis value of a 2002 FIFA World Cup soccer game and a 2002 Busan Asian game of the present invention soccer video.

Figure 17 is a contrast between the football video event detection method of the present invention and the event detection method suitable for foreign circumstances.

** Explanation of symbols on the main parts of the drawing **

10: video unit 20: video analysis unit

30: DL part 40: browsing tool part

50: detector

Hereinafter, a method of detecting an event of a soccer video according to the present invention will be described with reference to the accompanying drawings.

2 is a flowchart illustrating a soccer video event learning method according to the present invention, FIG. 3 is a flowchart illustrating a soccer video event detection method according to the present invention, and FIG. 4 is an enlargement and reduction of the soccer video screen of the present invention. Figure 5 is a symbol classification state diagram according to the degree, Figure 5 is a symbol classification state diagram according to the field position of the soccer video screen of the present invention, Figure 6 is a low-level information state diagram by the camera shooting target of the soccer video screen of the present invention, FIG. 7 is a diagram showing the 2002 FIFA World Cup and 2002 Busan Asian Games symbol form analysis value of the present invention, FIG. 8 is a diagram showing the distribution of 252 symbol scenes of the football video of the present invention, and FIG. FIG. 10 is a diagram showing an example of representative A, B, and C patterns of the soccer video of the present invention, and FIG. 12 is a state diagram of the event detection system of the soccer video of the present invention, FIG. 13 is a diagram showing 84 types of symbol analysis values of the soccer video of the present invention, and FIG. FIG. 15 is a diagram showing the distribution of 84 symbols and 108 symbols of the present invention soccer video, and FIG. 16 is a 2002 FIFA World Cup football and a 2002 Busan Asian film. As a result of experimenting the game, it is a diagram showing the analysis value of the number of shooting event detection, Figure 17 is a contrast between the event detection method suitable for a foreign country and the football video event detection method of the present invention.

Referring to FIG. 2, a method of learning a soccer video event according to the present invention is to learn whether to train various event occurrence patterns (PATTERN) for distinguishing an important scene (SHOT) of a soccer video. 1 course (S110),

In the case of learning the event pattern determined in the learning process 1 (S110), each scene is converted into a symbol SYMBOL, and the soccer video scene is enlarged and reduced to a value of 3 and a field position of 7 is given. , Learning 2 process of giving 2 value as camera's shooting target value, giving 2 value as playing speed value, and reflecting 2 value as the value of graphic information. (S120),

Determining an OBSERVATION SEQUENCE from the transformed symbol SYMBOL in the learning process 2 (S120). The observing sequence includes seven symbols including the current event scene symbol, the previous three symbols, and the following three symbols. Learning 3 course to determine with (S130),

Using the observation sequence determined in the learning process 3 (S130), the event generation pattern is processed by the Hidden Markov Model (HMM: HIDDEN MARKOV MODEL), and the Hidden Markov Model (HMM) is forwarded to the BAUM (FORWARD). PROCESS) and BACKWARD PROCESS to train (TRAINING) to reduce the amount of computation is composed of four processes.

In addition, referring to FIG. 3, a method for detecting a soccer video event according to the present invention will be described. A detection process 1 (S210) for determining whether to detect (DETECT) each event that distinguishes an important scene of a soccer video,

In the case of detecting an event determined in the detection process 1 (S210), by converting each scene into a symbol (SYMBOL), a value of 3 is given to the degree of zooming in and out of a soccer video scene, and a value of 7 is given to a field position. Detection 2 process of giving 2 value as camera's shooting target value, 2 as playback speed value, and reflecting 2 value as the use value of graphic information and analyzing them into 252 scene-type symbols ( S220),

The detection sequence 3 determines the observation sequence from the symbol transformed in the detection process 2 (S220), and determines the observation sequence as seven symbols including the current event scene symbol, the previous three symbols, and the subsequent three symbols. S230),

The detection sequence is determined by using the observation sequence determined in the detection process 3 (S230), and the detection process 4 (S240) detects a coupling probability from the hidden Markov model (HMM) learning process result.

Hereinafter, an event learning and detection method of a soccer video according to the present invention having the above configuration will be described in detail with reference to the accompanying drawings.

In general, a computer (PC) is a model that converts the input signal to facilitate processing, it is effective to use the deterministic model (DETERMINISTIC MODEL), the noise (ISE) and the distortion (DISTORTION) using the specific signals already known or secured There are two types of statistical models to solve.

There is a Markov model as a statistical model, but since only processing for an observable signal is possible, there are many limitations in the application.

To solve the above problem, the Hidden Markov Model (HMM), which is a dual STOCHASTIC model, enables the signal pattern to express the distortion and similarity of the pattern among various classes.

The Hidden Markov Model (HMM) provides an appropriate solution with its own modeling and learning capabilities, even when it does not have accurate knowledge of problem solving, and eliminates noise, loss and distortion. There is this.

In the soccer video, since the information pattern of the intermediate level is generated according to the time flow before and after the event, the hidden Markov model is used to probabilistically model the change over time.

When it is desired to learn various event patterns PATTERN generated from the soccer video already captured and secured by the video as described above (S110), each event scene is converted into 252 symbols (SYMBOL) (S120).

For example, each event generated from the soccer video is set as a symbol. For example, three screens, such as GLOBAL / LONG SHOT, MEDIUM / WOOM-IN SHOT, and CLOSE-UP SHOT, may be used. The value is set to 3 because it is divided into symbols, and the location of the field is, for example, a penalty area of each goal post, each of the left and right corners, and the midfield area (MID-FIELD). It is divided into 7 symbols by AREA) and gives a value of 7.

As described above, the actual situation of symbol division based on the enlargement and reduction of the screen is as shown in FIG. 4, and the actual situation of symbol division by field positions is shown in FIG. 5.

In addition, a value of 3 is given in order to classify the camera object into three symbols of a field, a player, or an audience, and the playback speed is a motion vector or a gradual scene. It is judged by the editing effect of GRADUAL SHOT TRANSITION, and it is given a value of 2 to divide it into two symbols of the normal speed playback and the replay screen played at a slow speed. The value of 2 is given to separate the use of into two symbols.

It is for automatically determining what is being photographed by the camera, and the intensity distribution of edges, the segment length, the distribution of the direction, and the color tone of the hue. The frequency distribution and the like are used, and the presence or absence of graphic information is used by the edge information and the high frequency distribution.

The attached FIG. 6 shows low-level information and an edge histogram extraction state of photographing state and graphic information for distinguishing a camera photographing object, and FIG. 7 shows 10 games of the 2002 FIFA World Cup and 2002 Busan Asian Games. Is a value obtained by analyzing 252 symbols, and FIG. 8 is a scene shape distribution diagram using 252 symbol values of a soccer video.

The events that occur in the soccer video above are GOAL, SHOOTING, CORNER KICK, FREE KICK, PENALTY KICK, Foul, etc. For example, the event detected from 10 soccer video games.

Table 1

event goal Shooting corner kick free kick Penalty kick Foul Detection value / 10 game 20/10 215/10 108/10 215/10 2/10 328/10 Average 2/1 22/1 11/1 22/1 0-2 / 1 33/1

According to Table 1, when the shooting event occurs 215 times in 10 randomly selected soccer games, and when the shooting event occurs, for example, the previous three scenes (SHOT) and the scene after the event and the event occurs Three scenes are determined as one observation sequence (S130).

The 215 sequences are represented by the A pattern, the B pattern, and the C pattern, and a large number of other patterns, as shown in FIG. 9, wherein the A, B, and C patterns occupy 80% or more of the total events. FIG. 10 shows an example of representative A, B, and C patterns as described above.

In FIG. 10, the A pattern that appears when the shooting event occurs, the game is interrupted and resumed in the corner area before the event occurs, followed by a close-up of the player who shoots after shooting. The replay scene is a continuous pattern. The replay scene also includes a scene in which the ball flies toward the goal post, a scene in which the player kicks the ball in front of the goal post.

In FIG. 10, the B pattern is similar to the A pattern, but a shooting event occurs after a corner corner is generated.

The replay shows the players fighting in front of the goalpost before the corner is taken, then replays the shooting scenes following the corner, shows the player, followed by the replay, and shows the progress of the game over a distance. Shows a player kicking a ball from close range.

In FIG. 10, the C pattern has a pattern before an event occurs, and the goal area is photographed at a long distance, followed by a near scene, and then a shooting occurs at a far scene, and the ball passes to the opponent goal area. After shooting the game, the shooting player is CLOSE-UP, followed by a REPLAY scene, and the game is played after the replay scene. Show you from a distance

Through the above analysis, A, B, C pattern is progressed by various attack methods before the shooting occurs, and shows that a certain scene occurs to some extent after the event occurs.

FIG. 11 shows the distribution of each pattern of the shooting event. In the soccer game, the pattern in which the shooting occurs has a high specific gravity of the A, B, and C patterns. .

That is, when the shooting event occurs, a total of four patterns exist in the sequence before and after the shooting event, with the main three patterns of the A, B, and C patterns and the rest as one pattern, but there are exceptions in these patterns. It can be seen that a lot of phenomenon occurs.

However, it can be seen that there are not many cases where a shooting event does not occur in a sequence similar to the pattern of the shooting event. As a method of learning and modeling a pattern in a temporal flow, the distortion and similarity of the pattern can be expressed. A hidden Markov model having a feature capable of fast processing while absorbing noise or distortion is used (S140).

When the event learning of the soccer video is completed as described above, it is determined whether an event is detected (S210), and when the event is to be detected by the determination, each scene is converted into a symbol SYMBOL as in the event learning process. In operation S220, the observation sequence is determined in the same manner as in the event learning process in operation S230.

When the observation sequence is determined as described above (S230), the result of the learning process by the hidden Markov model is input, and the coupling probability is detected as follows.

(Mathematical formula)

P (O | λ) = P (O ₁ , O ₂ , .., O _T | λ)

λ = {A, B, π}

A = {aij}, aij = P {qj (t + 1) | qi (t)}: State transition probability distribution

B = {bj (k), bj (k) = P {vk (t) | qj (t)}: Observation symbol probability distribution in state J

π = (πi}, πi = P {qi (1)}: Initial state probability distribution

As described above, each process of learning and detecting an event from a soccer video can be more easily understood with reference to FIG. 12.

That is, it shows that the learning process and the detection process are processed according to their respective order, and finally, the event is detected in the detection process using the results learned in the learning process.

As described above, each scene is defined as 252 symbols (SYMBOL) from the soccer video, but in reality, when the shooting target of the camera is an audience or a bench, the shot (SHOT) forms are formed regardless of the position information on the field. If a symbol is defined and position information on a field is defined as one symbol even in a scene of close-up of a player, the entire symbol can be defined as 84 symbols, and the definition of the symbol is shown in FIG.

In addition, it is difficult to know the position information of the player in the close-up of the player, but when the player is the goalkeeper, it is possible to find out the goal area, and even if not the goalkeeper, it is possible to find the position of the currently close-up player in the front and rear scenes.

In this way, symbols of 252 scenes can be defined as symbols of 108 scenes, as shown in FIG.

As described above, in the case of the 252 symbol, the utilization ratio is low, and in the case of the symbols 84 and 108, the utilization ratio of the symbol is high and the overall distribution is not changed. have.

That is, the case where 84 symbols or 108 symbols are used rather than 252 symbols (SYMBOL) is used, but the processing pattern is simplified because the event occurrence pattern is simple, but it can be seen that there is no significant change in the event pattern.

As a result of experimenting with the 2002 FIFA World Cup Soccer and the 2002 Busan Asian Game using the present invention as described above, the number of shooting events detected is shown in FIG.

Conventional event detection method uses a state transition diagram, which does not reflect the characteristics of exceptions occurring in sports events, and shows a low detection rate. Statistical analysis of exceptions solves sequence recognition problems and improves detection rates.

The attached FIG. 17 shows a comparison diagram of an existing event detection method suitable for a foreign situation and an event detection method suitable for a domestic situation, and shows that the present invention is the most suitable method for a domestic broadcasting situation.

As described above, the present invention can be applied to various multimedia communication devices including a portable terminal capable of video communication in addition to wired / wireless TV broadcasting.

In particular, when the present invention is applied to a multimedia portable terminal that charges a communication fee in proportion to the amount of data transmission, the main highlight event is detected and transmitted instead of the entire soccer game content. It is possible to transmit a video signal to easily understand the contents.

Therefore, when using a communication device such as a multimedia portable terminal, since only the highlight event portion is detected and transmitted, the amount of data to be transmitted is reduced and communication costs are reduced, thereby improving the quality of service and video processing technology of the wireless communication device. And improve the usability and stability of the system.

The present invention having the above configuration has an industrial use effect of improving the event detection rate from the soccer video by the domestic video broadcasting situation using the hidden Markov model.

In addition, since each event is detected and provided at a high rate from the soccer video, there is a convenient effect in use to facilitate the analysis of the soccer game.

In addition, it detects only the important highlight events of the football game and enjoys in a short time, saving time, and has a convenient effect that can be applied to the multimedia communication equipment and transmitted.

In addition, in the multimedia wireless communication method that charges a communication fee in proportion to the amount of transmission data, there is a convenient effect that can easily grasp the entire content of the football game with a minimum cost and a short time.

Claims (10)

Learning 1 process to determine whether to learn the event pattern of the football video, Learning 2 process of converting each scene into a symbol when learning the event determined in the learning 1 process, A learning 3 process of determining an observation sequence from the symbols converted in the learning 2 process; The event learning method of the soccer video comprising a learning 4 process of learning the hidden Markov model learning process using the observation sequence determined in the learning 3 process. The method of claim 1, wherein the learning 2 process comprises: A method of learning an event of a football video, characterized by analyzing the football video scene with symbols of 252 scene types, reflecting the zoom level, field position, camera shooting target, playback speed, and use of graphic information. According to claim 1, wherein the learning 3 process, The event learning method of a soccer video comprising a current event scene symbol, the previous three symbols and the next three symbols as the observation sequence. According to claim 1, wherein the learning 4 process, A method of learning an event of a football video, characterized by reducing the amount of computation by learning the hidden Markov model by forward processing and backward processing of Baum. Detecting 1 process for determining whether to detect each event of the football video, Detecting 2 process of converting each scene into a symbol when detecting the event determined in the detecting step 1, A detection 3 process of determining an observation sequence from the symbols converted in the detection 2 process; And a detection 4 step of detecting a coupling probability from a hidden Markov model learning process result by using the observation sequence determined in the detection 3 step. The method of claim 5, wherein the detecting2 process comprises: A method of detecting an event of a football video, characterized in that it is analyzed by symbols of 252 scene types, reflecting the degree of zooming in and out of the video scene, field position, camera shooting target, playback speed, and graphic information. The method of claim 5, wherein the detecting step 3 comprises: The event detection method of a soccer video, comprising the current event scene symbol, the previous three symbols and the next three symbols as the observation sequence. The method of claim 5, wherein the detecting step 4 comprises: Detecting the combined probability from the hidden Markov model learning process result. An event detection method of a soccer video, the method comprising detecting an important highlight event of a video soccer game and transmitting it through wired / wireless television broadcasting and multimedia wireless communication equipment. The method of claim 9, wherein the wireless communication device, Event detection method of a soccer video comprising a mobile terminal for multimedia communication.