Relevant bibliographies by topics / Histogram of Optical Flow Magnitude Gradients

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Academic literature on the topic 'Histogram of Optical Flow Magnitude Gradients'

Author: Grafiati
Published: 11 December 2022
Last updated: 26 January 2023

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Journal articles on the topic "Histogram of Optical Flow Magnitude Gradients"

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Gopakumar, K., and S. S. Suni. "Fusing pyramid histogram of gradients and optical flow for hand gesture recognition." International Journal of Computational Vision and Robotics 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijcvr.2020.10027249.

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Suni, S. S., and K. Gopakumar. "Fusing pyramid histogram of gradients and optical flow for hand gesture recognition." International Journal of Computational Vision and Robotics 10, no. 5 (2020): 449. http://dx.doi.org/10.1504/ijcvr.2020.109396.

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Singh, Gajendra, Rajiv Kapoor, and Arun Khosla. "Optical Flow-Based Weighted Magnitude and Direction Histograms for the Detection of Abnormal Visual Events Using Combined Classifier." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 3 (July 2021): 12–30. http://dx.doi.org/10.4018/ijcini.20210701.oa2.

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Movement information of persons is a very vital feature for abnormality detection in crowded scenes. In this paper, a new method for detection of crowd escape event in video surveillance system is proposed. The proposed method detects abnormalities based on crowd motion pattern, considering both crowd motion magnitude and direction. Motion features are described by weighted-oriented histogram of optical flow magnitude (WOHOFM) and weighted-oriented histogram of optical flow direction (WOHOFD), which describes local motion pattern. The proposed method uses semi-supervised learning approach using combined classifier (KNN and K-Means) framework to detect abnormalities in motion pattern. The authors validate the effectiveness of the proposed approach on publicly available UMN, PETS2009, and Avanue datasets consisting of events like gathering, splitting, and running. The technique reported here has been found to outperform the recent findings reported in the literature.
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Fan, Xijian, and Tardi Tjahjadi. "A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences." Pattern Recognition 48, no. 11 (November 2015): 3407–16. http://dx.doi.org/10.1016/j.patcog.2015.04.025.

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Yamuna G., Karthika Pragadeeswari C. ,. "RIGID TRACKING FOR SCALE AND ROTATION VARYING TARGETS FROM MOVING CAMERA." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 24, 2021): 175–80. http://dx.doi.org/10.17762/itii.v9i2.327.

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Targets when move rapidly needed to be tracked in many significant fields such as in combat applications. Objects undergoes many scale changes and also undergoes rotation variance. The target when viewed from static position, the size becomes smaller as the target moves farther and farther. Tracking the targets needs more attention and this can be done by Improved optical flow to which feature extraction through Histogram of Oriented Gradients and Random Sample Consensus (RANSAC) algorithm for scale and rotation invariance is added. The performance of the method is measured by its computation time, accuracy and high true positive values and other related parameters simulated in MAT LAB.
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Hariyono, Joko, Van-Dung Hoang, and Kang-Hyun Jo. "Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/196415.

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This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells14×14pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.
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Dong, Suge, Daidi Hu, Ruijun Li, and Mingtao Ge. "Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors." Applied Sciences 9, no. 10 (May 24, 2019): 2126. http://dx.doi.org/10.3390/app9102126.

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Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference descriptors is proposed. First, the motion magnitude of each frame is estimated by optical flow, and the foreground region is determined according to each motion magnitude of the pixels; the trajectories are only extracted from behavior-related foreground regions. Second, in order to better describe the relative temporal information between different actions, a motion difference descriptor is introduced to describe the foreground trajectory, and the direction histogram of the motion difference is constructed by calculating the direction information of the motion difference per unit time of the trajectory point. Finally, a Fisher vector (FV) is used to encode histogram features to obtain video-level action features, and a support vector machine (SVM) is utilized to classify the action category. Experimental results show that this method can better extract the action-related trajectory, and it can improve the recognition accuracy by 7% compared to the traditional dense trajectory method.
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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (February 1, 2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network.
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Li, Sheliang, and Huaqi Chai. "Recognition of Teaching Features and Behaviors in Online Open Courses Based on Image Processing." Traitement du Signal 38, no. 1 (February 28, 2021): 155–64. http://dx.doi.org/10.18280/ts.380116.

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High-quality online open courses have a wide audience. To further improve the quality of these courses, it is critical to analyze the teaching behaviors in class, which are the manifestation of the overall quality of the teacher. Considering the popularity of image processing-based behavior recognition in many disciplines, this paper explores deep into the teaching features and behaviors in online open courses based on image processing. Firstly, a coding scale was designed for teaching behaviors in online open courses. Next, the principle of optical flow solving was explained for teaching video images. Then, a teaching behavior feature extraction model was established based on dual-flow deep CNN, and used to extract the key points of teacher body and the behavior features of the teacher. After that, a teaching behavior recognition method was developed combining histogram of oriented gradients (HOG) and support vector machine (SVM) to accurately allocate the teaching features and behaviors to the corresponding teaching links. Finally, the proposed model was proved effective through experiments. Based on the recognized teaching behaviors, the frequency and duration of such behaviors were subject to comparative analysis, revealing the teaching features in high-quality online open courses.
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Liu, Guocheng, Caixia Zhang, Qingyang Xu, Ruoshi Cheng, Yong Song, Xianfeng Yuan, and Jie Sun. "I3D-Shufflenet Based Human Action Recognition." Algorithms 13, no. 11 (November 18, 2020): 301. http://dx.doi.org/10.3390/a13110301.

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In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.
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Dissertations / Theses on the topic "Histogram of Optical Flow Magnitude Gradients"

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Nallaivarothayan, Hajananth. "Video based detection of normal and anomalous behaviour of individuals." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106947/1/Hajananth_Nallaivarothayan_Thesis.pdf.

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This PhD research has proposed novel computer vision and machine learning algorithms for the problem of video based anomalous event detection of individuals. Varieties of Hidden Markov Models were designed to model the temporal and spatial causalities of crowd behaviour. A Markov Random Field on top of a Gaussian Mixture Model is proposed to incorporate spatial context information during classification. Discriminative conditional random field methods are also proposed. Novel features are proposed to extract motion and appearance information. Most of the proposed approaches comprehensively outperform other techniques on publicly available datasets during the time of publications originating from the results.
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Kuřátko, Jiří. "Počítání lidí ve videu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255470.

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This master's thesis prepared the programme which is able to follow the trajectories of the movement of people and based on this to create various statistics. In practice it is an effective marketing tool which can be used for instance for customer flow analyses, optimal evaluation of opening hours, visitor traffic analyses and for a lot of other benefits. Histograms of oriented gradients, SVM classificator and optical flow monitoring were used to solve this problem. The method of multiple hypothesis tracking was selected for the association data. The system's quality was evaluated from the video footage of the street with the large concentration of pedestrians and from the school's camera system, where the movement in the corridor was monitored and the number of people counted.
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Klos, Dominik. "Počítání tlakových lahví v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236055.

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This thesis deals with an automatic counting of cylinders placed on the back of a truck using images taken by a camera mounted above the car. To achieve this goal, an SVM classifier based on HOG image descriptors has been trained to detect the cylinders. Further, a tracking method based on optical flow estimation has been designed to track the cylinders through image sequences. The result of the thesis is an application that counts bottles with precision 93,08 % placed on the truck and visualizes results of the detection.
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Book chapters on the topic "Histogram of Optical Flow Magnitude Gradients"

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Rashwan, Hatem A., Mahmoud A. Mohamed, Miguel Angel García, Bärbel Mertsching, and Domenec Puig. "Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients." In Lecture Notes in Computer Science, 354–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40602-7_38.

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