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Effectively collecting and combining the spatial\u2013temporal information is essential for describing a video in action recognition. In this research paper, we address the problem of human action recognition by combining handcrafted spatio-temporal features with deep spatial features. This paper proposes a novel method for recognizing human actions in video by combining handcrafted spatio-temporal texture features extracted by our proposed feature descriptor, Volume Local Derivative Gradient Ternary Patterns (VLDGTP), and deep spatial features extracted from a modified Inception-v4 network. To reduce the dimension and to get equal sized feature vectors, we employed the PCA dimensionality reduction technique for both types of features. Then the dimensionality-reduced feature vectors are combined and passed to the SVM classifier for action recognition. Extensive experimentation is carried out on three benchmark datasets: KTH, UCF-101, and HMDB-51 datasets. Our proposed HAR method (VLDGTP\u2009+\u2009DEEP_FEATURES) outperforms existing HAR methods on the KTH, UCF-101, and HMDB-51 datasets, achieving an accuracy of 98.33% for KTH, 97.10% for UCF-101, and 87.50% for HMDB-51 dataset, demonstrating its superior performance in action recognition tasks.<\/jats:p>","DOI":"10.1007\/s44230-025-00095-5","type":"journal-article","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T02:49:16Z","timestamp":1744598956000},"page":"123-150","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Combining Handcrafted Spatio-Temporal and Deep Spatial Features for Effective Human Action Recognition"],"prefix":"10.1007","volume":"5","author":[{"given":"R. Divya","family":"Rani","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. 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