{"type":"ScholarlyArticle","title":"Human Activity Encoding and Recognition Using Low-level Visual Features","authors":[{"name":"Zheshen Wang","slug":"zheshen-wang","given":"Zheshen","family":"Wang","affiliation":""},{"name":"Baoxin Li","slug":"baoxin-li","given":"Baoxin","family":"Li","affiliation":""}],"abstract":"Automatic recognition of human activities is among the key capabilities of many intelligent systems with vision/perception. Most existing approaches to this problem require sophisticated feature extraction before classification can be performed. This paper presents a novel approach for human action recognition using only simple low-level visual features: motion captured from direct frame differencing. A codebook of key poses is first created from the training data through unsupervised clustering. Videos of actions are then coded as sequences of super-frames, defined as the key poses augmented with discriminative attributes. A weighted-sequence distance is proposed for comparing two super-frame sequences, which is further wrapped as a kernel embedded in a SVM classifier for the final classification. Compared with conventional methods, our approach provides a flexible non-parametric sequential structure with a corresponding distance measure for human action representation and classification without requiring complex feature extraction. The effectiveness of our approach is demonstrated with the widely-used KTH human activity dataset, for which the proposed method outperforms the existing state-of-the-art.Zheshen Wang, Baoxin Li","doi":null,"url":"https://ijcai.org/proceedings/2009/311","pdf":"https://ijcai.org/proceedings/2009/0311.pdf","markdown":"https://ijcai.org/proceedings/2009/311.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/311","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":311,"pages":{"first":1876,"last":1882},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}