A compact representation of human actions by sliding coordinate coding

• Main Authors: DING, Runwei, SUN, Qianru, LIU, Mengyuan, LIU, Hong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Human action recognition; bag-of-words model; local feature; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/4451; https://ink.library.smu.edu.sg/context/sis_research/article/5454/viewcontent/1729881417746114.pdf
• Institution: Singapore Management University

Human action recognition remains challenging in realistic videos, where scale and viewpoint changes make the problem complicated. Many complex models have been developed to overcome these difficulties, while we explore using low-level features and typical classifiers to achieve the state-of-the-art performance. The baseline model of feature encoding for action recognition is bag-of-words model, which has shown high efficiency but ignores the arrangement of local features. Refined methods compensate for this problem by using a large number of co-occurrence descriptors or a concatenation of the local distributions in designed segments. In contrast, this article proposes to encode the relative position of visual words using a simple but very compact method called sliding coordinates coding (SCC). The SCC vector of each kind of word is only an eight-dimensional vector which is more compact than many of the spatial or spatial-temporal pooling methods in the literature. Our key observation is that the relative position is robust to the variations of video scale and view angle. Additionally, we design a temporal cutting scheme to define the margin of coding within video clips, since visual words far away from each other have little relationship. In experiments, four action data sets, including KTH, Rochester Activities, IXMAS, and UCF YouTube, are used for performance evaluation. Results show that our method achieves comparable or better performance than the state of the art, while using more compact and less complex models.