By C.V. Jawahar, Shiguang Shan
The three-volume set, inclusive of LNCS 9008, 9009, and 9010, includes rigorously reviewed and chosen papers awarded at 15 workshops held along with the twelfth Asian convention on laptop imaginative and prescient, ACCV 2014, in Singapore, in November 2014. The 153 complete papers provided have been chosen from a number of submissions. LNCS 9008 includes the papers chosen for the Workshop on Human Gait and motion research within the Wild, the second one foreign Workshop on colossal info in 3D machine imaginative and prescient, the Workshop on Deep studying on visible information, the Workshop on Scene knowing for independent platforms and the Workshop on strong neighborhood Descriptors for computing device imaginative and prescient. LNCS 9009 includes the papers chosen for the Workshop on rising issues on picture recovery and Enhancement, the 1st foreign Workshop on strong interpreting, the second one Workshop on User-Centred computing device imaginative and prescient, the foreign Workshop on Video Segmentation in machine imaginative and prescient, the Workshop: My vehicle Has Eyes: clever car with imaginative and prescient expertise, the 3rd Workshop on E-Heritage and the Workshop on laptop imaginative and prescient for Affective Computing. LNCS 9010 comprises the papers chosen for the Workshop on function and Similarity for desktop imaginative and prescient, the 3rd foreign Workshop on clever cellular and selfish imaginative and prescient and the Workshop on Human id for Surveillance.
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Extra info for Computer Vision - ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, Revised Selected Papers, Part I
A reliable bounding box should overlap with many others during its propagation. A track of a detected bounding box will be employed if the bounding box overlaps more than 50 % with any of h bounding boxes of at least n frames which have hand detection processed among L frames. In our experiments, h = 2, L = 15, k = 3, n = 2. Some example results of our method of hand detection and tracking are shown in Fig. 3. As shown in Fig. 3, we are able to not only compensate missing or undone detections but also remove false detections.
View-invariant estimation of height and stride for gait recognition. K. ) ECCV 2002. LNCS, vol. 2359, pp. 155–167. Springer, Heidelberg (2002) 4. : Silhouette transformation based on walking speed for gait identiﬁcation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 717–722. IEEE (2010) 5. : Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape conﬁguration. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp.
Only 2 top scored bounding boxes are shown. We track hands and keep hand tracks which overlap with at least 2 detection results. As the results, we can eliminate some failed detections and missing detections as well as obtain quite good hand bounding box sequences. H. Do and K. Yanai to obtain connected and more reliable hand regions. In our experiments, L is set to 15. We then apply Wang et al. ’s method to extract dense trajectory aligned motion features and our hand displacement features from the detecting and tracking results.