Deep keyframe detection in human action videos. In this chapter, an effective algorithm Apr 26, 2018 · Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. Human action recognition is an important topic in computer vision. Our key intuition is that the human action video can be well interpreted by only several compelling frames in a video. The blue curve represents the ground truth curve and the red curve represents the detected result curve. The massive advancement in modern technology has greatly influenced researchers to adopt deep learning models in the fields of computer vision and image-processing, particularly human action recognition. Based on this intuition, we draw support from the power-ful feature representation ability of CNNs for images and devise a deep key frame detection in human action videos network that reasons directly on the entire video frames. Feb 12, 2020 · Machine learning and deep learning are the latest artificial intelligence (AI) techniques applied in keyframe identification in human action videos [12] of various fields such as sports training Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. From these frames, we extract key points using Apr 26, 2018 · Figure 4. A number of them are not capable of constituting the entire shot. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action Deep keyframe detection in human action videos. The frame difference method identifies the keyframe by the dilution of sequential frames. - "Deep Keyframe Detection in Human Action Videos" Feb 25, 2023 · Feng Z Wang X Zhou J Du X (2024) MDJ: A multi-scale difference joint keyframe extraction algorithm for infrared surveillance video action recognition Digital Signal Processing 10. Yang H, Wang B, Lin S, Wipf D, Guo M, Guo B (2015) Unsupervised extraction of video highlights via robust recurrent auto-encoders. Dec 4, 2020 · We presented a key frame detection deep ConvNets framework which can automatically annotate the key frames in human action videos. xidian. From these frames, we extract key points using Apr 26, 2018 · Figure 2. Feb 12, 2020 · A key frame is a representative frame which includes the whole facts of the video collection. from publication: Deep Keyframe Detection in Human Action Videos | Detecting representative frames in Mar 17, 2020 · Deep Keyframe Detection in Human Action Videos 2018年发表，conclusion说是第一篇行为识别中用深度学校方法提取关键帧的文章。 这篇的做法使用UCF101数据集在没有关键帧标注下完成，使用LDA做标注，再用双流卷积网络去拟合LDA。 In this section, a review for depicting the extraction of human motion keyframe detection by various methods of keyframe detection and spatiotemporal video feature extracted keyframe detection is discussed in detail. Improving any of the steps above can effectively increase recognition accuracy and speed. Many methods have been developed to recognize human activity, which is frame detection. - "Deep Keyframe Detection in Human Action Videos" Aug 19, 2021 · DOI: 10. arXiv:1804. Automatically generating labels to train the deep keyframe detection framework. Key frames, which encompass Jan 30, 2022 · Nowadays, the demand for human–machine or object interaction is growing tremendously owing to its diverse applications. An overview of the key frame detection two-stream ConvNets framework: appearance and motion networks. The purple solid circle represents the key frames. 104469 148 (104469) Online publication date: May-2024 Jul 9, 2024 · Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See, Arid: A new dataset for recognizing action in the dark, in: Deep Learning for Human Activity Recognition: Second International Workshop, DL-HAR 2020, Held in Conjunction with IJCAI-PRICAI 2020, Proceedings 2, Kyoto, Japan, January 8, 2021, Springer, January 2021, pp. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action In this paper, we propose an efficient approach for activity recognition in videos with key frame extraction and deep learning architectures, named KFSENet. We present the most important deep learning models for recognizing human actions, and analyze them to provide the current progress of deep learning algorithms applied to solve human action recognition Jun 21, 2023 · Automatic keyframe detection from videos is an exercise in selecting scenes that can best summarize the content for long videos. This Figure 3. S. The main contributions of this paper include Apr 26, 2018 · Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. It is used for indexing, classification, evaluation, and retrieval of video. Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. 10021, 2018 Feb 24, 2017 · A novel action recognition method which is based on combining the effective description properties of Local Binary Patterns with the appearance invariance and adaptability of patch matching based methods is presented, which is suitable for real-time uses of simultaneous recovery of human action of several lengths and starting points. Although the proposed method has outperformed the two commonly used keyframe extraction methods, this study has a few limitations. g. Apr 26, 2018 · To this end, we introduce a deep two-stream ConvNet for key frame detection in videos that learns to directly predict the location of key frames. Google Scholar Apr 26, 2018 · Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. Firstly, it conducts comprehensive research on this field through Citespace and comprehensively introduce relevant dataset. Apr 26, 2018 · A deep two-stream ConvNet for key frame detection in videos that learns to directly predict the location of key frames and a new ConvNet framework, consisting of a summarizer and discriminator, that can detect key frames with high accuracy. Deep Keyframe Detection in Human Action Videos. Despite the progress of action recognition algorithms in trimmed videos, the majority of real-world videos are lengthy and untrimmed with sparse segments of interest. 2022. CoRR abs/1804. summarizing security footage, detecting different scenes used in music clips) in different industries Machine learning and deep learning are the latest artificial intelligence (AI) techniques applied in keyframe identification in human action videos [12] of various fields such as sports training Feb 24, 2017 · A real-time action recognition method that achieves state-of-the-art accuracy on both single-action and multi-action recognition and employs Hidden Markov Model (HMM) to analyze the temporal relationship of the detected key frames. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action category from all other categories. 1 Keyframe Extraction Methods. Figure 6. Providing a summary of the video is an important task to facilitate quick browsing and content summarization. edu. 9642132 Corpus ID: 245388303; Key frame and skeleton extraction for deep learning-based human action recognition @article{Phan2021KeyFA, title={Key frame and skeleton extraction for deep learning-based human action recognition}, author={Hai-Hong Phan and Trung Tin Nguyen and Huu Phuc Ngo and Huu-Nhan Nguyen and Do Minh Hieu and Cao Truong Tran and Bao Ngoc Vi from publication: Deep Keyframe Detection in Human Action Videos | Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human Jan 29, 2021 · Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease. Human action recognition is a well-studied problem in computer vision and on the other hand action quality assessment is researched and experimented comparatively low. The red frames surrounded with blue rectangular box represent the detected key frames. CV] 26 Apr 2018 1 School of Physics and Optoelectronic Engineering, Xidian University, China xyan@stu. 70 Aug 19, 2021 · Request PDF | On Aug 19, 2021, Hai-Hong Phan and others published Key frame and skeleton extraction for deep learning-based human action recognition | Find, read and cite all the research you need Mar 11, 2024 · Human action recognition in videos is a critical task with significant implications for numerous applications, including surveillance, sports analytics, and healthcare. The appearance stream ConNets are used to extract appearance information (the output of fc6-layer) and the motion stream ConvNets are used to extract the motion Aug 7, 2022 · This paper presents an overview of the current state-of-the-art in action recognition using video analysis with deep learning techniques. Based on this intuition, we draw support from the power-ful feature representation ability of CNNs for images and In this paper, we propose an efficient approach for activity recognition in videos with key frame extraction and deep learning architectures, named KFSENet. The first two rows show RGB sequence frames and their corresponding optical flow images. Feb 1, 2024 · Temporal Action Detection (TAD) aims to accurately capture each action interval in an untrimmed video and to understand human actions. Aug 2, 2021 · Finally, when finding the relevant area using the extracted action template, the proposed method successfully extracts proper keyframes from human action videos for video classification using deep neural networks. At present, existing research works on action recognition are still not ideal, when most of the video content is The experimental results prove the validity of spatio-temporal slice location selection method, the proposed algorithm can effectively solve the problems of information redundancy and key information missing in existing methods. The timeline gives the position of the frame as a percentile of the total number of frames in the video (best seen in colour). While the training data is generated taking many different human action videos into account, the trained CNN can Apr 26, 2018 · Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action Deep Keyframe Detection in Human Action Videos Xiang Yan1,2 , Syed Zulqarnain Gilani2 , Hanlin Qin1 , Mingtao Feng3 , Liang Zhang4 , and Ajmal Mian2 arXiv:1804. We presented a key frame detection deep ConvNets framework which can detect the key frames in human action videos. The proposed method combines edge detection, simple difference, adaptive thresholding and 1D and 2D average filter algorithms in a hierarchical method. Apr 26, 2018 · To this end, we introduce a deep two-stream ConvNet for key frame detection in videos that learns to directly predict the location of key frames. This paper comprehensively surveys the state-of-the-art techniques and models used for TAD task. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large Apr 26, 2018 · This paper proposes a key frame selection technique in a motion sequence of 2D frames based on gradient of optical flow to select the most important frames which characterize different actions, and extracts key points using pose estimation techniques and employ them further in an efficient Deep learning network to learn the action model. To this end Bibliographic details on Deep Keyframe Detection in Human Action Videos. 1016/j. Example of the key frame detection results and ground truths. 2021. This study conducts an in-depth analysis of various deep learning models to address this A Key-detail Motion Capturing Network (K-MCN) is proposed, which allows multi-scale modeling and fusion of spatial and temporal features extracted from key-motion frames, enabling the network to realize the interaction and supplement of multiscale spatiotemporal information. First, we propose a key frame selection technique in a motion sequence of 2D frames based on gradient of optical flow to select the most important frames which characterize different actions. videos,” CoRR, v ol. The existing algorithms generate relevant key frames, but additionally, they generate a few redundant key frames. media. Thereon, LDA is applied to all the feature vectors of all the classes of training videos to project the feature vectors to a low dimensional feature space (LDA space) and obtain the Download scientific diagram | Key frames detected by our deep keyframe detection network. Example responses of our network and the corresponding ground truth curves obtained by LDA learning for two UCF101 videos. 2. Visualizations of key frames detection. The last row shows the ground truth curves. Such frames can correctly represent a human action shot with less redundancy, which is helpful for video analysis task such as video summarization and subsequent action recognition. - "Deep Keyframe Detection in Human Action Videos" Dec 4, 2020 · Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action Oct 2, 2023 · Thus, this paper proposes an Efficient Human Action Recognition System (EHARS) using k-means clustering based keyframe extraction technique, which addresses the issue of computational inefficiency May 15, 2023 · This paper presents a supervised learning scheme that employs key-frame extraction to enhance the performance of pre-trained deep learning models for object detection in surveillance videos. To this end Deep Keyframe Detection in Human Action Videos Xiang Yan 1,2 , Syed Zulqarnain Gilani 2 , Hanlin Qin 1 , Mingtao Feng 3 , Liang Zhang 4 , and Ajmal Mian 2 1 School of Physics and Optoelectronic This includes a novel method to measure the quality of the actions performed in Olympic weightlifting using human action recognition in videos. [8] reviewed the action detection task in untrimmed videos with the emphasis on temporal action detection that aims to detect the start and end of action instances, and only a brief Feb 1, 2022 · In video summarization, the objective is to represent a video sequence using representative shots, or key shots [3], [4] while in action recognition, the objective is to classify a video sequence [5]. 10021. Developing supervised deep learning models requires a significant amount of annotated video frames as training data, which demands substantial human effort for preparation. 10021v1 [cs. Secondly, it summarizes three types of Jun 1, 2022 · DOI: 10. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. A. 102490 Corpus ID: 249422576; Extracting keyframes of breast ultrasound video using deep reinforcement learning @article{Huang2022ExtractingKO, title={Extracting keyframes of breast ultrasound video using deep reinforcement learning}, author={Ruobing Huang and Qilong Ying and Zehui Lin and Zijie Zheng and Long Tan and Guoxue Tang and Qi Zhang and Man Luo and Xiuwen Yi Feb 10, 2022 · Based on the aforementioned reasons, this research proposes a method for selecting keyframes and adaptive cropping input video for human action recognition (HAR) systems. The task of temporal activity detection in untrimmed videos aims to localize the temporal Nov 23, 2017 · To make the problem tractable, in the first stage we train a deep generative model that generates a human pose sequence from random noise. It is used for indexing, classification, evaluation, and retrieval of Deep Keyframe Detection in Human Action Video 任务： 人体行为视频的关键帧检测。 motivation： 现有的关键帧提取算法没有ground truth，如何根据视频序列关键帧的特点，自动生成关键帧的ground truth. A key frame is a representative frame which includes the whole facts of the video collection. In the second stage, a skeleton-to-image network is trained, which is used to generate a human action video given the complete human pose sequence generated in the first stage. 10021 (2018) a service of . cn 2 School of Computer Science and Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. Despite the satisfactory accuracies achieved by a number of methods, they often need Action recognition can be divided into three steps of input data processing, action representation and action classification. Based on this intuition, we draw support from the power-ful feature representation ability of CNNs for images and Apr 26, 2018 · Deep Keyframe Detection in Human Action Videos. With input data processing, many studies have shown that videos contain highly temporally redundant data, making it Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. . cn, hlqin@mail. Jul 25, 2022 · Vahdani et al. The resulting photos are used for automated works (e. devise a deep key frame detection in human action videos network that reasons directly on the entire video frames. abs/1804. Mian, “Deep keyframe detection in human action. dsp. This is due to the lack of datasets… Sep 1, 2021 · [7] Yan X, Gilani S Z, Qin H et al 2018 Deep keyframe detection in human action videos[J] arXiv preprint arXiv:1804. Preprint Google Scholar [8] Zhu Y Y and Zhou D 2004 An approach of key frame extraction based on video clustering [J] Computer Engineering 30 12-14. The challenge lies in creating models that are both precise in their recognition capabilities and efficient enough for practical use. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action Feb 12, 2020 · An effective algorithm primarily based on the fusion of deep features and histogram has been proposed to overcome issues and extracts the maximum relevant key frames by way of eliminating the vagueness of the choice of key frames. 1109/RIVF51545. X Yan, SZ Gilani, H Qin, M Feng, L Zhang, A Mian Multiagent path finding using deep reinforcement learning coupled Apr 26, 2018 · Detecting representative frames in videos based on human actions is quite challenging because of the combined factors of human pose in action and the background. The blue solid circle represents the temporal location of ground truth and the purple solid circle represents the key frame temporal location of the detected result. The appearance and motion information (the output of fc7 layer) are concatenated as a spatio-temporal feature vector. Based on this intuition, we draw support from the power- Sep 30, 2021 · Understanding human behavior and activity facilitates advancement of numerous real-world applications, and is critical for video analysis. Human-action recognition from videos is getting more popular due to its increasing role in many applications from surveillance to autonomous Mar 16, 2023 · Yan X, Gilani SZ, Qin H, Feng M, Zhang L, Mian A (2018) Deep keyframe detection in human action videos. Based on the frame-level video label, we devise a deep key frame detection in human action videos network that reasons directly on the entire video frames. In: Proceedings of the IEEE international conference on computer vision, pp 4633–4641 Sep 1, 2020 · The excellent short video key frame extraction technology [1,2,3,4] can greatly reduce the computational complexity of massive video data processing, and also improve the efficiency of people to quickly identify and query information from video data, and avoid unnecessary human and material waste. 2024. This paper addresses this problem and formulates the key frame detection as one of finding the video frames that optimally maximally contribute to differentiating the underlying action Figure 5. Our key idea is to automatically generate labeled data for the CNN learning using a supervised linear discriminant method. The appearance network operates on RGB frames, and the motion network operates on the optical flow represented as images. April 2018; Authors: Xiang Yan. eedilc hkcsuw pnpj wre dtdl cgxjk fhfcwrai wtrva epzpaf zmw
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