An improved smart key frame extraction algorithm for vehicle target recognition

Wang, Jianguo; Zeng, Cheng; Wang, Zhongsheng; Jiang, Kun

doi:10.1016/j.compeleceng.2021.107540

Cited by 18 publications

(12 citation statements)

References 4 publications

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“…The proposed FSIFD and the retained frames extraction based on clustering (RFEC) [ 9 ] are compared experimentally in terms of video continuity, frame numbers, and playing duration.…”

Section: Resultsmentioning

confidence: 99%

“…However, the accuracy of the model is low for video frames with nonlinear structure. The retained frame extracted based on clustering [ 9 ] has a small redundancy and a strong ability to reflect the original video, but the temporal sequence of each frame is not considered in the processing. The motion analysis method [ 10 ] takes into account the motion characteristics of objects, which has a strong universality.…”

Section: Introductionmentioning

confidence: 99%

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Night Vision Anti-Halation Method Based on Infrared and Visible Video Fusion

Guo

Wang

Yang

2022

Sensors

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In order to address the discontinuity caused by the direct application of the infrared and visible image fusion anti-halation method to a video, an efficient night vision anti-halation method based on video fusion is proposed. The designed frame selection based on inter-frame difference determines the optimal cosine angle threshold by analyzing the relation of cosine angle threshold with nonlinear correlation information entropy and de-frame rate. The proposed time-mark-based adaptive motion compensation constructs the same number of interpolation frames as the redundant frames by taking the retained frame number as a time stamp. At the same time, considering the motion vector of two adjacent retained frames as the benchmark, the adaptive weights are constructed according to the interframe differences between the interpolated frame and the last retained frame, then the motion vector of the interpolated frame is estimated. The experimental results show that the proposed frame selection strategy ensures the maximum safe frame removal under the premise of continuous video content at different vehicle speeds in various halation scenes. The frame numbers and playing duration of the fused video are consistent with that of the original video, and the content of the interpolated frame is highly synchronized with that of the corresponding original frames. The average FPS of video fusion in this work is about six times that in the frame-by-frame fusion, which effectively improves the anti-halation processing efficiency of video fusion.

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“…The proposed FSIFD and the retained frames extraction based on clustering (RFEC) [ 9 ] are compared experimentally in terms of video continuity, frame numbers, and playing duration.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Night Vision Anti-Halation Method Based on Infrared and Visible Video Fusion

Guo

Wang

Yang

2022

Sensors

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show abstract

“…At present, the common key frame extraction methods mainly include the key frame extraction algorithm [4,5] based on SIFT features, the clustering-based key frame extraction algorithm [6,7], and [8], a key frame extraction algorithm based on motion analysis, however, in the key frame extraction algorithm based on SIFT features in the video key frame extraction. The smooth edge targets cannot accurately extract the feature points and have poor real-time performance, thus affecting the integrity and effectiveness of the key frame extraction [9,10]. Clusteringbased key frame extraction algorithm generally needs to set the center and number of clusters in advance in the clustering process.…”

Section: The Dance Teaching Methods Based On Aimentioning

confidence: 99%

A convolutional neural network based online teaching method using edge-cloud computing platform

Liu

2023

J Cloud Comp

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Teaching has become a complex essential tool for students’ abilities, due to their different levels of learning and understanding. In the traditional offline teaching methods, dance teachers lack a target for students ‘classroom teaching. Furthermore, teachers have limited time, so they cannot take full care of each student’s learning needs according to their understanding and learning ability, which leads to the polarization of the learning effect. Because of this, this paper proposes an online teaching method based on Artificial Intelligence and edge calculation. In the first phase, standard teaching and student-recorded dance learning videos are conducted through the key frames extraction through a deep convolutional neural network. In the second phase, the extracted key frame images were then extracted for human key points using grid coding, and the fully convolutional neural network was used to predict the human posture. The guidance vector is used to correct the dance movements to achieve the purpose of online learning. The CNN model is distributed into two parts so that the training occurs at the cloud and prediction happens at the edge server. Moreover, the questionnaire was used to obtain the students’ learning status, understand their difficulties in dance learning, and record the corresponding dance teaching videos to make up for their weak links. Finally, the edge-cloud computing platform is used to help the training model learn quickly form vast amount of collected data. Our experiments show that the cloud-edge platform helps to support new teaching forms, enhance the platform’s overall application performance and intelligence level, and improve the online learning experience. The application of this paper can help dance students to achieve efficient learning.

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“…Also, given that the video data used in this paper were recorded from the facades of a building with very low textures, among other challenges, we can mention feature extraction and matching algorithms in thermal infrared frames. In this regard, instead of using Kanade-Lucas-Tomasi (KLT) feature tracker algorithms in keyframes extraction methods, the proposed method utilizes the Scale-Invariant Feature Transform (SIFT) algorithm and matching key points (Suhr, 2009;Kumar, 2018;Wang, 2022). The steps in the keyframe extraction method presented in this paper are as follows: (1) the ability to recognize and remove blur frames from a sequence of thermal infrared video recorded frames.…”

Section: Introductionmentioning

confidence: 99%

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

Motayyeb

Samadzadegan

Javan

et al. 2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Keyframes extraction is required and effective for the 3D reconstruction of objects from a thermal video sequence to increase geometric accuracy, reduce the volume of aerial triangulation calculations, and generate the dense point cloud. The primary goal and focus of this paper are to assess the effect of keyframes extraction from the thermal infrared video sequence on the geometric accuracy of the dense point cloud generated. The method of keyframes extraction of thermal infrared video presented in this paper consists of three basic steps. (A) The ability to identify and remove blur frames from non-blur frames in a sequence of recorded frames. (B) The ability to apply the standard baseline condition between sequence frames to establish the overlap condition and prevent the creation of degeneracy conditions. (C) Evaluating degeneracy conditions and keyframes extraction using Geometric Robust Information Criteria (GRIC). The performance evaluation criteria for keyframes extraction in the generation of the thermal infrared dense point cloud in this paper are to assess the increase in density of the generated three-dimensional point cloud and reduce reprojection error. Based on the results and assessments presented in this paper, using keyframes increases the density of the thermal infrared dense point cloud by about 0.03% to 0.10% of points per square meter. It reduces the reprojection error by about 0.005% of pixels (2 times).

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An improved smart key frame extraction algorithm for vehicle target recognition

Cited by 18 publications

References 4 publications

Night Vision Anti-Halation Method Based on Infrared and Visible Video Fusion

Night Vision Anti-Halation Method Based on Infrared and Visible Video Fusion

A convolutional neural network based online teaching method using edge-cloud computing platform

Effect of Keyframes Extraction From Thermal Infrared Video Stream to Generate Dense Point Cloud of the Building's Facade

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