Human activity recognition from UAV-captured video sequences

Human 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.

/pdf/human-action-recognition-with-bag-of-visual-words-using-3qmxscgwye.pdf

Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization

Recognizing human activities using automated methods has emerged recently as a pivotal research theme for security-related applications. In this research paper, an optical flow descriptor is proposed for the recognition of human actions by considering only features derived from the motion. The signature for the human action is composed as a histogram containing kinematic features which include the local and global traits.
Experimental results performed on the Weizmann and UCF101 databases confirmed the potentials of the proposed approach with attained classification rates of 98.76% and 70%, respectively, to distinguish between different human actions. For comparative and performance analysis, different types of classifiers including Knn, decision tree, SVM and deep learning are applied to the proposed descriptors. Further analysis is performed to assess the proposed descriptors under different resolutions and frame rates. The obtained results are in alignment with the early psychological studies reporting that human motion is adequate for the perception of human activities.

/pdf/human-activity-recognition-via-optical-flow-decomposing-51yzd4ulj1.pdf

Human activity recognition via optical flow: decomposing activities into basic actions

Automated detection and tracking of a person’s actions plays a vital role in surveillance systems. Human activity detection has been carried out by using a variety of features; including flow-based, spatio-temporal and interest points based. We have created a fusion of features by incorporating those which give better results. LBP, HOG, Haar wavelets, SIFT, velocity and displacement being the major ones. By employing the time efficiency and optimality of SMO to train SVM, we have trained our system for both single person and multi-human action classification with improved accuracy. A generalized hierarchy of actions has been presented in this paper to demonstrate the extension of our methodology. We have achieved an accuracy of 91.99% on combination of KTH and Weizmann dataset and 86.48% on multi-human dataset. We have introduced our self-generated multi-human activity dataset in the following paper.

Human activity recognition using mixture of heterogeneous features and sequential minimal optimization

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).

Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

An enhanced method for human action recognition

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An enhanced method for human action recognition