Semantic Recognition of Human-Object Interactions via Gaussian-Based Elliptical Modeling and Pixel-Level Labeling

IEEE Access

Yazeed Yasin Ghadi¹  Munkhjargal Gochoo²  Nida Khalid³  Ahmad Jalal³  Kibum Kim⁴ 

[1] Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, United Arab Emirates;Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates;Department of Computer Science, Air University, Islamabad, Pakistan;Department of Human-Computer Interaction, Hanyang University, Ansan, South Korea;

关键词: 3D point cloud;    fiducial points;    human-object interaction;    pixel labeling;    semantic segmentation;    super-pixels;   

DOI  :  10.1109/ACCESS.2021.3101716

来源: DOAJ

【 摘 要 】

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

【 授权许可】

Unknown