In This Paper, We Propose A Multiframe Marked Point Process Model Of Line Segments And Point Groups For Automatic Target Structure Extraction And Tracking In Inverse Synthetic Aperture Radar (ISAR) Image Sequences. To Deal With Scatterer Scintillations And High Speckle Noise In The ISAR Frames, We Obtain The Resulting Target Sequence By An Iterative Optimization Process, Which Simultaneously Considers The Observed Image Data And Various Prior Geometric Interaction Constraints Between The Target Appearances In The Consecutive Frames. A Detailed Quantitative Evaluation Is Performed On Eight Real ISAR Image Sequences Of Different Carrier Ships And Airplane Targets, Using A Test Database Containing 545 Manually Annotated Frames.
This Paper Proposes A Detailed Framework Of Storing Privacy Information In Surveillance Video As A Watermark. Authorized Personnel Is Not Only Removed From The Surveillance Video As In But Also Embedded Into The Video Itself, Which Can Only Be Retrieved With A Secrete Key. A Perceptual-model-based Compressed Domain Video Watermarking Scheme Is Proposed To Deal With The Huge Payload Problem In The Proposed Surveillance System. A Signature Is Also Embedded Into The Header Of The Video As In For Authentication. Simulation Results Have Shown That The Proposed Algorithm Can Embed All The Privacy Information Into The Video Without Affecting Its Visual Quality. As A Result, The Proposed Video Surveillance System Can Monitor The Unauthorized Persons In A Restricted Environment, Protect The Privacy Of The Authorized Persons But, At The Same Time, Allow The Privacy Information To Be Revealed In A Secure And Reliable Way.
With The Increasing Volume Of Video Data, How To Analyze And Browse Video In A Fast And Effective Way Has Become An Urgent Problem In Applications. This Letter Proposes A Novel Video Synopsis Method In Compressed Domain For Browsing Video Captured By Static Cameras. Synopsis Video Is A Video Abstraction, Which Displays Moving Objects From Different Periods Simultaneously On The Primary Background Contents Of Original Video. To Overcome The Low Efficiency Of Traditional Video Synopsis For Compressed Video, Our Method Presents A New Graph Cut Algorithm To Extract Objects Tubes And Meanwhile Gives A Fast Solution To Minimize Energy Function In Compressed Domain. Experimental Results In H.264 Video Have Demonstrated The High-efficiency Of This New Video Synopsis Scheme For Massive Video Browsing.
In Recent Years, Extensive Research Efforts Have Been Dedicated To Automatic News Content Analysis. In This Paper, We Propose A Novel Algorithm For Anchorperson Detection In News Video Sequences. In This Method, The Raw News Videos Are Firstly Split Into Shots By A Four-threshold Method, And The Key Frames Are Extracted From Each Shot. After That, The Anchorperson Detection Is Conducted From These Key Frames By Using A Clustering-based Method Based On A Statistical Distance Of Pearson's Correlation Coefficient. To Evaluate The Effectiveness Of The Proposed Method, We Have Conducted Experiments On 10 News Sequences. In These Experiments, The Proposed Scheme Achieves A Recall Of 0.96 And A Precision Of 0.97 For Anchorperson Detection.
In This Paper, We Consider A Multiobject Tracking Problem In Complex Scenes. Unlike Batch Tracking Systems Using Detections Of The Entire Sequence, We Propose A Novel Online Multiobject Tracking System In Order To Build Tracks Sequentially Using Online Provided Detections. To Track Objects Robustly Even Under Frequent Occlusions, The Proposed System Consists Of Three Main Parts: 1) Visual Tracking With A Novel Data Association With A Track Existence Probability By Associating Online Detections With The Corresponding Tracks Under Partial Occlusions; 2) Track Management To Associate Terminated Tracks For Linking Tracks Fragmented By Long-term Occlusions; And 3) Online Model Learning To Generate Discriminative Appearance Models For Successful Associations In Other Two Parts. Experimental Results Using Challenging Public Data Sets Show The Obvious Performance Improvement Of The Proposed System, Compared With Other State-of-the-art Tracking Systems. Furthermore, Extensive Performance Analysis Of The Three Main Parts Demonstrates Effects And Usefulness Of The Each Component For Multiobject Tracking.
This Paper Presents A Method To Detect Video Tampering And Distinguish It From Common Video Processing Operations, Such As Recompression, Noise, And Brightness Increase, Using A Practical Watermarking Scheme For Real-time Authentication Of Digital Video. In Our Method, The Watermark Signals Represent The Macro Block’s And Frame’s Indices, And Are Embedded Into The Nonzero Quantized Discrete Cosine Transform Value Of Blocks, Mostly The Last Nonzero Values, Enabling Our Method To Detect Spatial, Temporal, And Spatiotemporal Tampering. Our Method Can Be Easily Configured To Adjust Transparency, Robustness, And Capacity Of The System According To The Specific Application At Hand. In Addition, Our Method Takes Advantage Of Content-based Cryptography And Increases The Security Of The System. While Our Method Can Be Applied To Any Modern Video Codec, Including The Recently Released High-efficiency Video Coding Standard, We Have Implemented And Evaluated It Using The H.264/AVC Codec, And We Have Shown That Compared With The Existing Similar Methods, Which Also Embed Extra Bits Inside Video Frames, Our Method Causes Significantly Smaller Video Distortion, Leading To A PSNR Degradation Of About 0.88 DB And Structural Similarity Index Decrease Of 0.0090 With Only 0.05% Increase In Bitrate, And With The Bit Correct Rate Of 0.71 To 0.88 After H.264/AVC Recompression.
We Enhance Underexposed, Low Dynamic Range Videos By Adaptively And Independently Varying The Exposure At Each Photoreceptor In A Post-process. This Virtual Exposure Is A Dynamic Function Of Both The Spatial Neighborhood And Temporal History At Each Pixel. Temporal Integration Enables Us To Expand The Image’s Dynamic Range While Simultaneously Reducing Noise. Our Non-linear Exposure Variation And Denoising Filters Smoothly Transition From Temporal To Spatial For Moving Scene Elements. Our Virtual Exposure Framework Also Supports Temporally Coherent Per Frame Tone Mapping. Our System Outputs Restored Video Sequences With Significantly Reduced Noise, Increased Exposure Time Of Dark Pixels, Intact Motion, And Improved Details.
This System Presents A New Video Motion Descriptor Based On A Multi-scale Video Segmentation To Provide A Multilayered Output As Well As Connections With The Rich Interactions That Occur Between Objects At The Semantic Level. We Also Put The Emphasis On Relationships Between Motion Clusters By Providing A New Relative Motion Descriptor Encapsulating Relative Motion Patterns Within A Local Spatio-temporal Neighborhood. Experimental Results On The Challenging TRECVID MED11 Event Recognition Dataset Validate The Approach.
Most Global Motion Estimation (GME) Methods Are Oriented To Video Coding While Video Object Segmentation Methods Either Assume No Global Motion (GM) Or Directly Adopt A Coding Oriented Method To Compensate For GM. This Paper Proposes A Hierarchical Differential GME Method Oriented To Video Object Segmentation. A Scheme Which Combines Three-step Search And Motion Parameters Prediction Is Proposed For Initial Estimation To Increase Efficiency. A Robust Estimator That Uses Object Information To Reject Outliers Introduced By Local Motion Is Also Proposed. For The First Frame, When The Object Information Is Unavailable, A Robust Estimator Is Proposed Which Rejects Outliers By Examining Their Distribution In Local Neighborhoods Of The Error Between The Current And The Motion-compensated Previous Frame. Subjective And Objective Results Show That The Proposed Method Is More Robust, More Oriented To Video Object Segmentation, And Faster Than The Referenced Methods.
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