Combining Pose-Invariant Kinematic Features and Object Context Features for RGB-D Action Recognition

Abstract

Action recognition using RGB-D cameras is a popular research topic. Recognising actions in a pose-invariant manner is very challenging due to view changes, posture changes and huge intra-class variations. This study aims to propose a novel pose-invariant action recognition framework based on kinematic features and object context features. Using RGB, depth and skeletal joints, the proposed framework extracts a novel set of pose-invariant motion kinematic features based on 3D scene flow and captures the motion of body parts with respect to the body. The obtained features are converted to a human body centric space that allows partial viewinvariant recognition of actions. The proposed pose-invariant kinematic features are extracted for both foreground (RGB and depth) and skeleton joints and separate classifiers are trained. Bordacount based classifier decision fusion is employed to obtain an action recognition result. For capturing object context features, a convolutional neural network (CNN) classifier is proposed to identify the involved objects. The proposed context features also include temporal information on object interaction and help in obtaining a final action recognition. The proposed framework works even with non-upright human postures and allows simultaneous action recognition for multiple people, which are topics that remain comparatively unresearched. The performance and robustness of the proposed pose-invariant action recognition framework are tested on several benchmark datasets. We also show that the proposed method works in real-time.

Fusion Mechanisms for Human Activity Recognition Using Automated Machine Learning

“…Acc. Ramanathan et al (2019) [26] 41.37 Shahroudy et al (2016) [28] 70.30 [80] 85. 40 Das et al (2019) [76] 92.…”

“…The authors in [26] combine motion and temporal object context features. RGB-D data and body joints are used to extract a novel set of pose-invariant motion kinematic features that are converted to a human body centric space.…”

“…50 Wang et al (2012) [24] 85. 75 Ramanathan et al (2019) [26] 90.84 Zhu et al (2018) [77] 93.00 Althloothi et al (2014) [25] 93. 10 Jalal et al (2017) [23] 94.…”

A Comparative Analysis of Decision-Level Fusion for Multimodal Driver Behaviour Understanding

“…A theoretical study of such methods from the pre-Deep Learning era is provided in [26]. Rank-level decisionlevel fusion, such as Borda Count voting [27], [28], [29] and Reciprocal Rank Voting [30] are less popular, but have been successfully applied in the field of biometric identification [31], [32]. There are several works targeting multimodal fusion through learning-based methods, e.g., using SVM, LSTM, or neural network fusion layers [33], [34], [35], [7], [36], [37].…”