@article{MAKHILLJEAS2019141918482,
    title = {Computer Vision Methods for Looking at Peopleinteracting with Objects:
A Taxonomy and Survey},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {19},
    pages = {7223-7233},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.7223.7233},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.7223.7233},
    author = {Sultan},
    keywords = {Human-object interaction methods,human-object interaction datasets,object recogni-tion,human
action,human pose estimation1,publicly,taxonomic classifications},
    abstract = {Human-object interaction recognition is a challenging problem as it is a combination of three
challenging tasks in computer vision, namely human-action recognition, object detection and the scene
understating. These tasks share many challenges such as the appearance of a human performing a specific
action can be a rich source of information and indication about the type of the performed action. other
challenges such as occlusions, the layout of the scene, variation of body pose, and object appearance make
it very important to understand to distinguish between two similar actions. The scope of this study is limited
to actions were humans interacting with objects. Therefore, we introduce a new taxonomic classifications for
human-object interaction. Also, we present a number of approaches that have been introduced recently that
can be applied to a real-word applications. Finally, we present a number of human-object interaction datasets
that are publicly available.}
    }