Aslan, Muhammet FatihDurdu, AkifSabancı, Kadir2021-12-132021-12-1320200941-06431433-3058https://doi.org/10.1007/s00521-019-04365-9https://hdl.handle.net/20.500.13091/154Human activity recognition (HAR) has quite a wide range of applications. Due to its widespread use, new studies have been developed to improve the HAR performance. In this study, HAR is carried out using the commonly preferred KTH and Weizmann dataset, as well as a dataset which we created. Speeded up robust features (SURF) are used to extract features from these datasets. These features are reinforced with bag of visual words (BoVW). Different from the studies in the literature that use similar methods, SURF descriptors are extracted from binary images as well as grayscale images. Moreover, four different machine learning (ML) methods such as k-nearest neighbors, decision tree, support vector machine and naive Bayes are used for classification of BoVW features. Hyperparameter optimization is used to set the hyperparameters of these ML methods. As a result, ML methods are compared with each other through a comparison with the activity recognition performances of binary and grayscale image features. The results show that if the contrast of the environment decreases when a human enters the frame, the SURF of the binary image are more effective than the SURF of the gray image for HAR.eninfo:eu-repo/semantics/openAccessHuman Activity RecognitionImage ProcessingSpeeded Up Robust FeaturesBag Of Visual WordsMachine LearningK-Nearest NeighborsDecision TreeSupport Vector MachineNaive BayesHyperparameter OptimizationRecognizing Human ActionsRobust ApproachContextSystemImageArticle10.1007/s00521-019-04365-92-s2.0-85069803484