TY - JOUR AU - Fahad Shahbaz Khan AU - Muhammad Anwer Rao AU - Joost Van de Weijer AU - Andrew Bagdanov AU - Antonio Lopez AU - Michael Felsberg PY - 2013// TI - Coloring Action Recognition in Still Images T2 - IJCV JO - International Journal of Computer Vision SP - 205 EP - 221 VL - 105 IS - 3 PB - Springer US N2 - In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. SN - 0920-5691 L1 - http://refbase.cvc.uab.es/files/krw2013.pdf UR - http://dx.doi.org/10.1007/s11263-013-0633-0 N1 - CIC; ADAS; 600.057; 600.048 ID - Fahad Shahbaz Khan2013 ER -