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RESEARCH REPORT N? 8050 August 2012 Project-Teams LEAR and NPR ASIA ISBN INRIA/RR--8050--FR+ENG Hung Wang, Alexander Kl?SER, Cordelia Schmidt, Cheng-Lin Liu ISSN 0249-6399 hal-00725627, version 2
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How to fill out dense trajectories and motion?

01
Start by understanding the concept: Dense trajectories and motion refer to the analysis and tracking of objects in videos or image sequences. It involves capturing and tracking the movement of objects frame by frame.
02
Preprocess the video data: Before filling out dense trajectories and motion, it is essential to preprocess the video data. This includes tasks like resizing, scaling, converting to grayscale, or applying filters to improve the quality of the video.
03
Extract dense trajectories: The first step in filling out dense trajectories and motion is to extract dense trajectories from the video. This involves selecting interest points in each frame and tracking them across multiple frames. There are numerous algorithms available for dense trajectory extraction, such as Improved Dense Trajectories (IDT) or Trajectory-based Recognition and Structure Extraction (T-RASE).
04
Compute motion features: Once the dense trajectories are extracted, the next step is to compute motion features for each trajectory. These features can include optical flow, speed, direction, or acceleration. Motion features provide valuable information about the movement patterns and dynamics of objects in the video.
05
Fill out a dense trajectories and motion dataset: After computing motion features, create a dataset that includes all the extracted dense trajectories and their corresponding motion features. This dataset will be used for further analysis, such as action recognition, object detection, or video summarization.

Who needs dense trajectories and motion?

01
Computer vision researchers: Dense trajectories and motion are essential in computer vision research, specifically in tasks like action recognition, object tracking, or behavior analysis. Researchers can use dense trajectory information to understand and extract meaningful insights from videos.
02
Video analytics companies: Companies that deal with video analytics, surveillance, or video summarization can benefit from using dense trajectories and motion. By employing these techniques, they can develop robust algorithms for video understanding, event detection, or anomaly detection in real-time video streams.
03
Sports analysts: Dense trajectories and motion find significant applications in sports analysis, where tracking and understanding player movements are crucial. By employing dense trajectory and motion analysis, sports analysts can gain valuable insights into player strategies, team dynamics, or tactical decision-making.
Overall, dense trajectories and motion analysis are valuable techniques for various fields, including computer vision research, video analytics, and sports analysis. By understanding how to fill out dense trajectories and motion and identifying who needs these techniques, one can leverage their benefits in various applications.
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Dense trajectories and motion refer to the techniques used in computer vision and video analysis to track and analyze the movement of objects in a video sequence.
There is no specific requirement for filing dense trajectories and motion as it is a technical concept used in computer vision and video analysis, rather than a formal filing process.
Dense trajectories and motion are not something that needs to be filled out. They are techniques used by researchers and professionals in the field of computer vision and video analysis to extract and analyze motion information from video sequences.
The purpose of dense trajectories and motion is to track and analyze the movement of objects in videos, allowing researchers and professionals to understand and interpret various actions and activities.
There is no specific information that needs to be reported on dense trajectories and motion. It is a technique used for analysis and does not involve reporting specific information.
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