Key Frame Extraction Based on Frame Difference and Cluster for Person Re-identification

2021 ◽  
Author(s):  
Yiyin Ding ◽  
Shaoqi Hou ◽  
Xu Yang ◽  
Wenyi Du ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Key Frame Extraction ◽  
Key Frame ◽  
Frame Difference

scholarly journals A Fast and Robust Key Frame Extraction Method for Video Copyright Protection

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yunyu Shi ◽  
Haisheng Yang ◽  
Ming Gong ◽  
Xiang Liu ◽  
Yongxiang Xia
Keyword(s):  
Copyright Protection ◽  
Extraction Method ◽  
Video Sequence ◽  
Characteristic Difference ◽  
Original Sequence ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Frame Sequence ◽  
Frame Difference ◽  
Key Frames

The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on.

scholarly journals Large-Scale Video Retrieval via Deep Local Convolutional Features

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chen Zhang ◽  
Bin Hu ◽  
Yucong Suo ◽  
Zhiqiang Zou ◽  
Yimu Ji
Keyword(s):  
Large Scale ◽  
Video Retrieval ◽  
Video Data ◽  
Query Image ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Storage Cost ◽  
Extraction Algorithm ◽  
Feature Aggregation ◽  
And Storage

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

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