Video based action detection and recognition human using optical flow and SVM classifier

Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on skin color segmentation and Support Vector Machine(SVM) is proposed. Firstly, segmenting image using color model to filter candidate faces roughly; And then Eye-analogue segments at a given scale are discovered by finding regions which are darker than their neighborhoods to filter candidate faces farther; at the end, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. Our tests in this paper are based on MIT face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

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Classification of Action Based Video using Heterogeneous Feature Extraction and SVM

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2089.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1887-1892

Keyword(s):

Optical Flow ◽

Video Sequence ◽

Human Action ◽

Video Data ◽

Support Vector ◽

Svm Classifier ◽

Video Frames ◽

Integral Role ◽

Heterogeneous Feature

Action recognition (AR) plays a fundamental role in computer vision and video analysis. We are witnessing an astronomical increase of video data on the web and it is difficult to recognize the action in video due to different view point of camera. For AR in video sequence, it depends upon appearance in frame and optical flow in frames of video. In video spatial and temporal components of video frames features play integral role for better classification of action in videos. In the proposed system, RGB frames and optical flow frames are used for AR with the help of Convolutional Neural Network (CNN) pre-trained model Alex-Net extract features from fc7 layer. Support vector machine (SVM) classifier is used for the classification of AR in videos. For classification purpose, HMDB51 dataset have been used which includes 51 Classes of human action. The dataset is divided into 51 action categories. Using SVM classifier, extracted features are used for classification and achieved best result 95.6% accuracy as compared to other techniques of the state-of- art.v

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A Low-Cost Monocular Vision-Based Obstacle Avoidance Using SVM and Optical Flow

Unmanned Systems ◽

10.1142/s2301385018500097 ◽

2018 ◽

Vol 06 (04) ◽

pp. 267-275

Author(s):

Ajay Shankar ◽

Mayank Vatsa ◽

P. B. Sujit

Keyword(s):

Optical Flow ◽

Autonomous Navigation ◽

Low Cost ◽

Obstacle Detection ◽

Monocular Vision ◽

Support Vector ◽

Svm Classifier ◽

Range Finding ◽

Image Patches ◽

Computational Resources

Development of low-cost robots with the capability to detect and avoid obstacles along their path is essential for autonomous navigation. These robots have limited computational resources and payload capacity. Further, existing direct range-finding methods have the trade-off of complexity against range. In this paper, we propose a vision-based system for obstacle detection which is lightweight and useful for low-cost robots. Currently, monocular vision approaches used in the literature suffer from various environmental constraints such as texture and color. To mitigate these limitations, a novel algorithm is proposed, termed as Pyramid Histogram of Oriented Optical Flow ([Formula: see text]-HOOF), which distinctly captures motion vectors from local image patches and provides a robust descriptor capable of discriminating obstacles from nonobstacles. A support vector machine (SVM) classifier that uses [Formula: see text]-HOOF for real-time obstacle classification is utilized. To avoid obstacles, a behavior-based collision avoidance mechanism is designed that updates the probability of encountering an obstacle while navigating. The proposed approach depends only on the relative motion of the robot with respect to its surroundings, and therefore is suitable for both indoor and outdoor applications and has been validated through simulated and hardware experiments.

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Simultaneous Utilization of Inertial and Video Sensing for Action Detection and Recognition in Continuous Action Streams

IEEE Sensors Journal ◽

10.1109/jsen.2020.2973361 ◽

2020 ◽

Vol 20 (11) ◽

pp. 6055-6063 ◽

Cited By ~ 4

Author(s):

Haoran Wei ◽

Nasser Kehtarnavaz

Keyword(s):

Continuous Action ◽

Action Detection ◽

Simultaneous Utilization ◽

Detection And Recognition

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An Improved Micro-Expression Recognition Method Based on Necessary Morphological Patches

Symmetry ◽

10.3390/sym11040497 ◽

2019 ◽

Vol 11 (4) ◽

pp. 497 ◽

Cited By ~ 3

Author(s):

Yue Zhao ◽

Jiancheng Xu

Keyword(s):

Optical Flow ◽

Support Vector ◽

Svm Classifier ◽

Expression Recognition ◽

Optical Flow Method ◽

Psychological Analysis ◽

Emotional Representation ◽

Discriminant Ability ◽

Casme Ii ◽

Micro Expression

Micro-expression is a spontaneous emotional representation that is not controlled by logic. A micro-expression is both transitory (short duration) and subtle (small intensity), so it is difficult to detect in people. Micro-expression detection is widely used in the fields of psychological analysis, criminal justice and human-computer interaction. Additionally, like traditional facial expressions, micro-expressions also have local muscle movement. Psychologists have shown micro-expressions have necessary morphological patches (NMPs), which are triggered by emotion. Furthermore, the objective of this paper is to sort and filter these NMPs and extract features from NMPs to train classifiers to recognize micro-expressions. Firstly, we use the optical flow method to compare the on-set frame and the apex frame of the micro-expression sequences. By doing this, we could find facial active patches. Secondly, to find the NMPs of micro-expressions, this study calculates the local binary pattern from three orthogonal planes (LBP-TOP) operators and cascades them with optical flow histograms to form the fusion features of the active patches. Finally, a random forest feature selection (RFFS) algorithm is used to identify the NMPs and to characterize them via support vector machine (SVM) classifier. We evaluated the proposed method on two popular publicly available databases: CASME II and SMIC. Results show that NMPs are statistically determined and contribute to significant discriminant ability instead of holistic utilization of all facial regions.

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