scholarly journals CGA: a new feature selection model for visual human action recognition

2020 ◽  
Author(s):  
Ritam Guha ◽  
Ali Hussain Khan ◽  
Pawan Kumar Singh ◽  
Ram Sarkar ◽  
Debotosh Bhattacharjee
Keyword(s):  
Feature Selection ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Selection Model ◽  
New Feature

Feature Extraction and Representation for Distributed Multi-View Human Action Recognition

2013 ◽  
Vol 3 (2) ◽  
pp. 145-154 ◽  
Author(s):  
Jiajia Luo ◽  
Wei Wang ◽  
Hairong Qi
Keyword(s):  
Action Recognition ◽  
Approximation Error ◽  
Human Action Recognition ◽  
Human Action ◽  
Base Station ◽  
Feature Representation ◽  
Superior Performance ◽  
Feature Descriptor ◽  
Testing Stage ◽  
New Feature

Multi-view human action recognition has gained a lot of attention in recent years for its superior performance as compared to single view recognition. In this paper, we propose a new framework for the real-time realization of human action recognition in distributed camera networks (DCNs). We first present a new feature descriptor (Mltp-hist) that is tolerant to illumination change, robust in homogeneous region and computationally efficient. Taking advantage of the proposed Mltp-hist, the noninformative 3-D patches generated from the background can be further removed automatically that effectively highlights the foreground patches. Next, a new feature representation method based on sparse coding is presented to generate the histogram representation of local videos to be transmitted to the base station for classification. Due to the sparse representation of extracted features, the approximation error is reduced. Finally, at the base station, a probability model is produced to fuse the information from various views and a class label is assigned accordingly. Compared to the existing algorithms, the proposed framework has three advantages while having less requirements on memory and bandwidth consumption: 1) no preprocessing is required; 2) communication among cameras is unnecessary; and 3) positions and orientations of cameras do not need to be fixed. We further evaluate the proposed framework on the most popular multi-view action dataset IXMAS. Experimental results indicate that our proposed framework repeatedly achieves state-of-the-art results when various numbers of views are tested. In addition, our approach is tolerant to the various combination of views and benefit from introducing more views at the testing stage. Especially, our results are still satisfactory even when large misalignment exists between the training and testing samples.

Realistic Human Action Recognition With Multimodal Feature Selection and Fusion

2013 ◽  
Vol 43 (4) ◽  
pp. 875-885 ◽  
Author(s):  
Qiuxia Wu ◽  
Zhiyong Wang ◽  
Feiqi Deng ◽  
Zheru Chi ◽  
David Dagan Feng
Keyword(s):  
Feature Selection ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

scholarly journals CGA: A New Feature Selection Model for Visual Human Action Recognition

2020 ◽  
Author(s):  
Ritam Guha ◽  
Ali Hussain Kha ◽  
Pawan Kumar Singh ◽  
Ram Sarkar
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Vector ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Image Understanding ◽  
Recognition Mechanism ◽  
New Feature ◽  
Original Feature

Abstract Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel Feature Selection (FS) model called Co-operative Genetic Algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of co-operative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets such as Weizmann, KTH, UCF11, HMDB51 and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering nearly 50 to 60% of the original feature vector.

scholarly journals Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7941
Author(s):  
Seemab Khan ◽  
Muhammad Attique Khan ◽  
Majed Alhaisoni ◽  
Usman Tariq ◽  
Hwan-Seung Yong ◽  
...  
Keyword(s):  
Feature Selection ◽  
Deep Learning ◽  
Action Recognition ◽  
Feature Fusion ◽  
Research Work ◽  
Human Action Recognition ◽  
Human Action ◽  
Computational Time ◽  
Features Selection ◽  
Feature Mapping

Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.

A framework of human action recognition using length control features fusion and weighted entropy-variances based feature selection

2021 ◽  
Vol 106 ◽  
pp. 104090
Author(s):  
Farhat Afza ◽  
Muhammad Attique Khan ◽  
Muhammad Sharif ◽  
Seifedine Kadry ◽  
Gunasekaran Manogaran ◽  
...  
Keyword(s):  
Feature Selection ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Features Fusion ◽  
Weighted Entropy

scholarly journals Composite Feature Vector Assisted Human Action Recognition through Supervised Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 1556-1566
Keyword(s):  
Feature Extraction ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Research Direction ◽  
Human Action ◽  
Support Vector ◽  
Complex Wavelet Transform ◽  
Simulation Experiments ◽  
Composite Feature ◽  
New Feature

Human Action Recognition is a key research direction and also a trending topic in several fields like machine learning, computer vision and other fields. The main objective of this research is to recognize the human action in image of video. However, the existing approaches have many limitations like low recognition accuracy and non-robustness. Hence, this paper focused to develop a novel and robust Human Action Recognition framework. In this framework, we proposed a new feature extraction technique based on the Gabor Transform and Dual Tree Complex Wavelet Transform. These two feature extraction techniques helps in the extraction of perfect discriminative features by which the actions present in the image or video are correctly recognized. Later, the proposed framework accomplished the Support Vector Machine algorithm as a classifier. Simulation experiments are conducted over two standard datasets such as KTH and Weizmann. Experimental results reveal that the proposed framework achieves better performance compared to state-of-art recognition methods.

scholarly journals Efficient Sparse Representation based Action Recognition in video

2019 ◽  
Vol 9 (2) ◽  
pp. 728-732
Keyword(s):  
Sparse Representation ◽  
Action Recognition ◽  
Real Life ◽  
Human Action Recognition ◽  
Human Action ◽  
Feature Representation ◽  
Svm Classifier ◽  
Interest Points ◽  
Frame Sequence ◽  
New Feature

Human Action Recognition (HAR) is an interesting and helpful topic in various real-life applications such as surveillance based security system, computer vision and robotics. The selected features and feature representation methods, classification algorithms decides the accuracy of the HAR systems. A new feature called, Skeletonized STIP (Spatio Temporal Interest Points) is identified and used in this work. The skeletonization on the action video’s foreground frames are performed and the new feature is generated as STIP values of the skeleton frame sequence. Then the feature set is used for initial dictionary construction in sparse coding. The data for action recognition is huge, since the feature set is represented using the sparse representation. To refine the sparse representation the max pooling method is used and the action recognition is performed using SVM classifier. The proposed approach outperforms on the benchmark datasets.

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