Vector space based augmented structural kinematic feature descriptor for human activity recognition in videos

Abstract

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

Sensor Data Acquisition and Multimodal Sensor Fusion for Human Activity Recognition Using Deep Learning

“…As wearable devices are widely used nowadays, Human Activity Recognition (HAR) has become an emerging research area in mobile and wearable computing. Recognizing human activity leads to a deep understanding of individuals’ activity patterns or long-term habits, which contributes to developing a wide range of user-centric applications such as human-computer interaction [1,2], surveillance [3,4], video streaming [5,6], AR/VR [7], and healthcare systems [8,9]. Although activity recognition has been investigated to a great extent in computer vision area [10,11,12], the application is limited to a certain scenario that equips pre-installed cameras with sufficient resolution and guaranteed angle of view.…”

Zero-Shot Human Activity Recognition Using Non-Visual Sensors

“…Furthermore, activity recognition has been widely reported in many fields using sensor modalities, including ambient sensors [35], wearable sensors [36], smart phones [34], and smart watches [37]. Those sensors contribute to developing a wide range of application domains such as sport [38], human-computer interaction [39], surveillance [40], video streaming [41], healthcare system [42], and computer vision area [43]. Due to the properties of noninvasive sensors, some studies discussed how to monitor human activities using this type of sensors (i.e., non-visual sensors) because they are both easy to install and privacy preserving [44,45].…”

Human Activity Recognition Using Gaussian Mixture Hidden Conditional Random Fields

“…Most of the existing works [18][19][20][21][22][23] focused on feature extraction and selection; however, very limited works have been done for the recognition module. Some studies exploited conventional techniques [24][25][26][27][28]. Among them, HMM is one of the best candidates for the activity recognition; however, HMM is generative in nature and less precise than its matching part like HCRF model [29].…”