article · Big Data Mining and Analytics
Human action recognition aims to identify human activities within images and video recordings. A core difficulty lies in creating action descriptors that remain dependable across varied visual environments. A method addressing this combines spatial and spectral filtering techniques. Scale-invariant visual features are extracted using a Difference of Gaussian filter, while spectral characteristics are gathered using a Difference of Wavelet filter. Merging these two outputs creates a unified feature vector for each action image. Linear Discriminant Analysis then reduces dimensionality to remove redundant data, followed by classification using a nearest neighbour approach. Evaluated through five-fold cross-validation on benchmark datasets, the combined feature model achieved average recognition accuracies of 83.66 percent on the Weizmann dataset and 62.52 percent on the UCF 11 dataset, outperforming the individual filters used in isolation.
Automated human action recognition allows computer vision systems to understand human movements across varied visual conditions. Improving how movement features are extracted from video frames helps systems handle changes in scale and background. By joining spatial and frequency-based image filtering, recognition accuracy can be enhanced without relying on single, less dependable visual cues.
The work represents early-stage algorithmic development evaluated on academic benchmark datasets. Potential applications include automated video analysis and monitoring tools that require action detection from recorded visual media. Potential users include software developers building computer vision pipelines. Given the modest accuracy on complex real-world data such as UCF 11, the approach remains far from market deployment.
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Human Action Recognition (HAR) attempts to recognize the human action from images and videos. The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments. A novel action descriptor is proposed in this study, based on two independent spatial and spectral filters. The proposed descriptor uses a Difference of Gaussian (DoG) filter to extract scale-invariant features and a Difference of Wavelet (DoW) filter to extract spectral information. To create a composite feature vector for a particular test action picture, the Discriminant of Guassian (DoG) and Difference of Wavelet (DoW) features are combined. Linear Discriminant Analysis (LDA), a widely used dimensionality reduction technique, is also used to eliminate duplicate data. Finally, a closest neighbor method is used to classify the dataset. Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy, and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well. The average accuracy of DoG + DoW is observed as 83.6635% while the average accuracy of Discrinanat of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 80.2312% and 77.4215%, respectively. The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG + DoW is observed as 62.5231% while the average accuracy of Difference of Guassian (DoG) and Difference of Wavelet (DoW) is observed as 60.3214% and 58.1247%, respectively. From the above accuracy observations, the accuracy of Weizmann is high compared to the accuracy of UCF 11, hence verifying the effectiveness in the improvisation of recognition accuracy.
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DOI: 10.26599/bdma.2022.9020040
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