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Natural scene video identification method

A technology of video recognition and natural scenes, which is applied in the field of computer vision, can solve the problems of static or dynamic, bad influence on recognition effect, and reduce the effectiveness of extracted features, so as to improve the effect of recognition

Inactive Publication Date: 2016-10-05
SUN YAT SEN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

However, since behavioral videos in natural environments usually face a greater degree of multi-object occlusion, shadows, background noise, and dramatic changes in illumination, scale, and viewing angles, feature extraction has become a serious problem. In addition, due to There are no restrictions on the shooting process, so the camera may be static or dynamic, and the two states are mixed in an unpredictable way. In particular, when the camera moves relative to the background, the action features will be identified by the motion Together with the cluttered background, this will significantly reduce the effectiveness of the extracted features, thus adversely affecting the recognition effect

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Embodiment 1

[0038] Embodiment 1: Specific steps of a natural scene video recognition method of the present invention

[0039] Such as figure 1 , 2 As shown in 3, the specific steps of a natural scene video recognition method of the present invention are as follows:

[0040] 1) Generate candidate feature point trajectories through feature point tracking, and then use the trajectory clipping method based on trajectory dissimilarity measure and ROI detection to remove trajectories caused by feature point mismatch or background changes, and finally target the reliable trajectory after clipping Calculate and extract a series of trajectory descriptors that are invariant to scale, translation, and rotation;

[0041] Among them, the steps of the trajectory pruning method as a measure of trajectory dissimilarity are as follows:

[0042] A1: Suppose there are N trajectories starting from frame f: T={t i }, i=1,...,N, for each track, define a track segment with a time window of 5 frames And adjacent frame...

Embodiment 2

[0059] Embodiment 2: Recognition effect experiment of a natural scene video recognition method of the present invention

[0060] 1. Experimental data set: including UCF sports data set and YouTube data set;

[0061] 2. Experimental environment: Matlab 2008a platform;

[0062] 3. Experimental toolbox: Kanade-Lucas-Tomasi feature tracker, VLFeat open source library and Dollar behavior recognition toolbox;

[0063] 4. Experimental method: In each experiment, first select a group of motion video sequences performed by the same actor from the sample set as test data, and use the rest of the sequences as training data. Repeat this process so that each set of motion sequences in the data set has Once used as the test data, specifically, for the YouTube data set, it is divided into 25 subsets, of which 24 subsets are used for training, and the remaining 1 subset is used for testing; for the UCF sports data set, 1 of them is video The fragments are used for testing and the rest are used for t...

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Abstract

The invention belongs to the field of computer vision and particularly relates to a natural scene video identification method. The method specifically comprises the following steps of 1) generating a characteristic point track descriptor; 2) generating a local time-space descriptor; 3) representing a video sequence by a bag-of-words model; 4) predicting a state of a camera; and 5) selecting adaptive characteristic fusion. According to the method, a track dissimilarity measurement and ROI detection-based method is adopted, so that characteristic point tracks from a background are effectively removed; and an adaptive characteristic fusion method is proposed, and the two descriptors are selectively combined according to the dynamic or static state of the camera, so that the identification effect of an algorithm is remarkably improved.

Description

Technical field [0001] The invention belongs to the field of computer vision, and particularly relates to a natural scene video recognition method. Background technique [0002] Human behavior recognition is an important research direction of human motion analysis. It is a high-level application of computer vision and is widely used in intelligent surveillance systems, advanced human-computer interaction, content-based video retrieval and motion analysis. The current research interest in human behavior recognition has shifted from simple behavior recognition under the well-controlled shooting environment to more realistic behavior recognition in unconstrained environments (also known as "natural environments") such as story films, sports broadcast videos, and home videos. . Behavior recognition in this environment is challenging because of the huge changes caused by camera movement, background speckles, and changes in lighting conditions, scales, and viewing angles. The main dif...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/42G06F18/23213
Inventor 衣杨关山周晓聪龙东阳陈弟虎
Owner SUN YAT SEN UNIV