Dissertation Defense Presentation
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1 Dissertation Defense Presentation Feature Based Video Sequence Identification by Kok Meng Pua Committee : Prof. Susan Gauch (Chair) Prof. John Gauch Prof. Joseph Evans Prof. Jerry James Prof. Tom Schreiber May 17, 2002 EECS, Kansas University 1
2 Agenda Driving Problem The Goals Our Solution Related Work and Pilot Work The Video Identification Technique Design Approach Experimental Results and Discussion Conclusion and Future Work May 17, 2002 EECS, Kansas University 2
3 Driving Problem Television and video streaming over the Internet enable us to see latest events and stories Large portion of these news and video sequences are repeated Issues: inefficient use of storage media in archives ineffective use of a viewer s time Need: a fully automated topic-based video tracking system May 17, 2002 EECS, Kansas University 3
4 The Goals Design and develop a feature-based automatic video sequence identification and tracking technique real time video processing video stream domain independence Not a topic-based tracking system Goals: real time video sequence identification and tracking technique with high accuracy lossless compression - reduce storage requirement by keeping only unique video sequences May 17, 2002 EECS, Kansas University 4
5 Technique: Our Solutions Combination of video hashing and frame by frame video comparison Two standalone systems were implemented a) Video Processing System Video Feature Abstraction and video sequence segmentation b)video Sequence Identification System Video Sequence Hashing Video Sequence Comparison Video Archiving and Tracking Located repeated video sequences with high accuracy (>90% in recall) Video input domain independence May 17, 2002 EECS, Kansas University 5
6 Related Work Image Abstraction and Similarity Measure Methods Researchers Description Color histogram Image Partitioning with Color Moment Color Coherent Vector Dominant Color Color Moments Flickner 95 (IBM) Pentland 95 (MIT) Huang & Pass 97 (Cornell) Stricker 96 (Swiss Federal Institution) Pass & Zabih 96 (Cornell) Swain 91 Chang & Smith 96 (Columbia) -Global color distribution of an image -Easy to compute and insensitive to small changes in viewing position - Divide an image into five overlapping regions -Partition pixel value (histogram bucket) based upon their spatial coherence -Fast to compute -a few main colors -less storage size -easy to compute -less storage size -more robust than histogram May 17, 2002 EECS, Kansas University 6
7 Related Work - Video Sequence Abstraction & Similarity Measure Methods Key frames Centroid Feature Vector Rframe Video Signature Frame-by-Frame (9 Color Moments) Researchers Zhang 95 (NUS) Wu 98 (Zhejiang University) Arman 94 (Princeton) Cheung 00 (California University) J. Gauch 99 (KU) Description -Map the entire video sequence to some small representative images -Use still images as key frames -created from key frames that composed of 35 numbers (32 color features and 3 texture features) -Less storage and faster similarity computation -A body (10 th frame of the video sequence, values of four motion tracking regions, and size of sequence -Shape (moment invariant) and color (histogram) properties of Rframes -9 signature frames per video -very expensive -frame by frame comparison -less storage size but preserve uniqueness of video May 17, 2002 EECS, Kansas University 7
8 Pilot Work The VISION Digital Library System test bed for evaluating automatic and comprehensive mechanisms for library creation (video segmentation) and content-based search, retrieval, filtering and browsing of video across networks. support real time content-based video scene detection and segmentation using combination of video, audio and closed caption. Video Seeking System (VIDSEEK) web-enabled digital video library browsing system. dynamic-on-demand clustering allows users to organize the video clips based on multiple user-specified video features (color, shape, category) category-based browsing allows users to interactively and dynamically filter the VISION digital video library clips based on a given set of constraints (video source, keywords and date of capture) VidWatch Project (Video Authentication) color moment based frame-by-frame video information processing technique Detect and record video content difference between two television channels May 17, 2002 EECS, Kansas University 8
9 Four Main Processes of The Video Sequence Identification and Tracking System Video Input Stream Video Abstraction Extraction Video Stream Segmentation Video Processing and Stream Segmentation Input Video Sequences Video Frame Hashing Potential Similar Video Sequences Video Sequence Hashing Video Sequence Filtering Similar Video Sequences Frame-by-Frame Video Sequence Comparison Video Sequence Comparison Labelled Repeat or New Video Sequences Video Sequence Archving & Tracking Video Sequence Archiving & Tracking May 17, 2002 EECS, Kansas University 9
10 Video Processing and Stream Segmentation Video Processing System (VPS) A standalone real time video segmentation system on NT platform Use Osprey digitizer board for video stream digitization. Two main functions : video sequence creation and video abstraction extraction May 17, 2002 EECS, Kansas University 10
11 Video Processing and Stream Segmentation Video Sequence Abstraction Compare abstraction for every frame in the video sequence but not key frames only Video abstraction method used should require small storage and also easy comparison computation. Use first 3 color moments of each primary color component (Red, Blue, and Green) 3 moments are the mean, standard deviation, and skew of each color component of a video frame. 9 floating points per video frame May 17, 2002 EECS, Kansas University 11
12 Video Processing and Stream Segmentation Video Sequence Creation A video sequence is a video shot ( an image sequence that represents continuous action and corresponds to a single action of the camera) Use video segmentation technique developed in VISION project Shot boundary detection : combination of average brightness, intensity difference and histogram difference of adjacent frames. Look at the differences between two frames dt distance from each other to detect shot boundary with smoother (slow) transition Meta-information captured: Size of video sequence Video Sequence identifier (date and time the sequence is captured) May 17, 2002 EECS, Kansas University 12
13 Video Sequence Hashing Process Purpose: Reduce the size of number of sequences requiring frame-byframe comparison by selecting only similar video sequences which have many similar frames Video Sequence Hashing Process Input Video Sequences (frame Video Processing and Stream Segmentation Process) Potential similar video sequences Video Frame Hashing Similar Video Sequence Filtering No Potential Similar Video Sequence No Similar Video Sequence New Sequence Detected Similar Video Sequences (to Video Sequence Comparison Process) May 17, 2002 EECS, Kansas University 13
14 Video Sequence Hashing Process Video Frame Hashing: Map video frames into similar frame buckets and hash similar frames instead of identical video frames Each video frame is represented by a color moment string Color Moment String map 9 original (10 to 1 mapping) floating numbers into 9 integers and concatenate them to become a character string Use concatenation of all digits of the 9 original float numbers( moment values) as the hashing key proved to be ineffective due to noise introduced by both transmission and digitization into the video source. Identical values unlikely for repeated video broadcast May 17, 2002 EECS, Kansas University 14
15 Example of Color Moment String Mapping Original nine Color Moment Numbers M(Red) M(Green) M(Blue) S(Red) S(Green) S(Blue) Skew(Red) Skew(Green) Skew(Blue) Mapping Process Resulting Integer Numbers Integer Number Concatenation Color Moment String (Hashing Key) May 17, 2002 EECS, Kansas University 15
16 Video Sequence Hashing Process Video Frame Hashing: Mapping Ratio Issues: Mapping ratio too small : actual identical frames fall into difference similar buckets Mapping ratio too large : none-identical frames fall into same similar buckets Experimental result showed 1% of mapping error (identical frames into different similar buckets) May 17, 2002 EECS, Kansas University 16
17 Video Sequence Hashing Process Video Frame Hashing Cost Estimation: Total video frame hashing cost for an input video sequence Cost(m) = [H(n) + L(n)] for n = 1.m m = size (total video frame count) of the video sequence H(n) = hashing cost L(n) = linked list traversal cost Independent of hash table size (video archive size) Experimental results showed an average hashing time of 500ms for each input video sequence May 17, 2002 EECS, Kansas University 17
18 Video Sequence Hashing Process Video Sequence Filtering : Second component of similar video hashing process Purpose: Identify truly similar video sequences Potential similar video sequences are filtered to remove sequences whose degree of similarity is below some threshold Two video sequences are similar if: 1. The size difference of the two video sequences is less than 10% 2. The percentage of frames in the two video sequences that have identical color moment strings is > 30% (overlap threshold) May 17, 2002 EECS, Kansas University 18
19 Video Sequence Comparison Process Video Sequence hashing results in false positive matches: Color moment strings used for hashing are built from approximate color moment values consider only the percentage of similar video frames and ignore their temporal ordering A more accurate frame-by-frame comparison is required Sum of the absolute moment difference of two sequences is calculated One video sequences is detected as a repeat of the other if sum of their absolute moment difference < moment difference threshold (10.0) May 17, 2002 EECS, Kansas University 19
20 An Example of Video Identification Process Hash Bucket (Color Moment String) Linked list of Sequence Index Data Input Video Sequence Q1 Starting Frame A B C D E F G H I Ending Frame J A B B1 B1 C B1 D A1 C1 E B1 Video Sequence A1 T D H U M Z P F B1 Video Sequence B1 A B C R E F G H I G H B1 A1 B1 Video Index Table C1 I B1 Video Sequence C1 T D H U M Z P M A1 C1 P A1 B1 Note : Each block is a video frame with its color moment string represented by an alphabet Step 1 : Video Frame Hashing T R U Z A1 B1 A1 A1 C1 C1 C1 Video Hash Table Hashing Output List (Sequence Q1) Sequence A1 Sequence C1 Sequence B1 Matching Similar Frames =2 Matching Similar Frames =2 Matching Similar Frames =8 Step 2 : Video Sequence Filtering Filtering Output List (Sequence Q1) Sequence B1 Video Frame Hashing Output List Video Sequence Filtering Output List May 17, 2002 EECS, Kansas University 20
21 Purposes Video Sequence Archiving and Tracking Record sequence identification results Number of video sequences processed Number of detected new and repeat sequences Control and enable video sliding window function Allow the total video archive to grow until it contains 24 hours worth of video sequences Oldest/expired sequences are dropped from video archive as the newer sequences are added Use a video index table to capture identification results and keep the total video archive within the 24 hours window size May 17, 2002 EECS, Kansas University 21
22 Input video source: Experimental Results 32 hours of video stream as primary input source A second 24 hours of difference video source for video identification technique input domain independent validation Measurements : Video hashing accuracy and efficiency measures Video hashing time Select an optimum overlap threshold value Video sequence comparison cost Overall video identification accuracy and efficiency Recall and precision measurement Achievable storage compression Validating video identification technique with different video source May 17, 2002 EECS, Kansas University 22
23 Video Hashing Time vs. Video Archive Size May 17, 2002 EECS, Kansas University 23
24 Video Hashing Time vs. Video Sequence Size May 17, 2002 EECS, Kansas University 24
25 Frame-by-Frame Video Sequence Comparison Cost May 17, 2002 EECS, Kansas University 25
26 Experiment Results Recall and Precision Calculation Recall The ratio of the number of correctly detected repeated sequences to the total number of true repeated sequences in the video archive Recall = true positives / (true positives + false negatives) Precision The ratio of the number of correctly detected repeated sequences to the number of detected repeated sequences in the video archive Precision = true positives / (true positives + false positives ) Term definition: True positive : detect repeat, and it is repeat True negative : detect new, and it is new False positive : detect repeat, and it was new False negative : detect new, and it was repeat May 17, 2002 EECS, Kansas University 26
27 Example of Recall and Precision Calculation Inputs : S1.1, S1.2, S1.3,S1.4, S2.1, S2.2, S2.3 S1.2, S1.3 & S1.4 are repeat of S1.1 Also, S2.2 & S2.3 are repeat of S2.1 Outputs: S1.1 : Detected New S1.2 : Detected Repeat of S1.1 S1.3 : Detected New S1.4 : Detected Repeat of S1.1, S1.2 & S1.3 S2.1 : Detected New S2.2 : Detected Repeat of S2.1 S2.3 : Detected Repeat of S2.1 & S1.2 Recall = true positives / (true positives + false negatives) = 6 / (6+2) = 0.75 Precision = true positives / (true positives + false positives) = 6 / (6+1) = 0.85 May 17, 2002 EECS, Kansas University 27
28 Choosing An Optimum Overlap Threshold Value Overlap threshold : percentage of total similar video frames (identical color moment string) over the total video frames of a video sequence Recall = true positives / (true positives + false negatives) = total correctly detected repeated sequence / total true repeated sequence Precision = true positives / (true positives + false positives ) = total correctly detected repeated sequence / total detected similar video sequences May 17, 2002 EECS, Kansas University 28
29 Overall Video Identification Performance Measurement -Total of 32 hours of video source (2831 sequences) new and 1603 repeat in a 24 hour sliding window May 17, 2002 EECS, Kansas University 29
30 Achievable Compression Ratio May 17, 2002 EECS, Kansas University 30
31 Validating Video Identification Technique with difference Video Source -Total of 24 hours of video source (3394 sequences) new and 1456 repeat May 17, 2002 EECS, Kansas University 31
32 Achievable Storage Compression Ratio May 17, 2002 EECS, Kansas University 32
33 Conclusion Summary This thesis reports on a video sequence identification and tracking technique that can be used to process continuous video streams, identify unique sequences and remove repeated sequences The algorithm described is efficient (runs in real time) and effective Accurately locate repeated sequences (recall >90% and precision >89%) for two different video streams Achieve a compression gain factor of approximately 30% for both video streams May 17, 2002 EECS, Kansas University 33
34 Future Work Technique Improvement Partial Matching Detection of subset of a known video sequence or superset of few sequences overlapping one another Disk based Hashing Handle a very large video window size to detect and track repeated video sequences that occur farther apart in time User Application Web-enabled Video Stream Browsing System Enable users to search and browse the video archive and view selected video sequences Story based Video Sequence Identification Group video sequences into different stories using video abstraction such as closed caption, audio and video content. May 17, 2002 EECS, Kansas University 34
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