By C.V. Jawahar, Shiguang Shan
The three-volume set, together with LNCS 9008, 9009, and 9010, comprises conscientiously reviewed and chosen papers offered at 15 workshops held along with the twelfth Asian convention on desktop imaginative and prescient, ACCV 2014, in Singapore, in November 2014. The 153 complete papers offered have been chosen from a number of submissions. LNCS 9008 comprises the papers chosen for the Workshop on Human Gait and motion research within the Wild, the second one overseas Workshop on sizeable info in 3D laptop imaginative and prescient, the Workshop on Deep studying on visible facts, the Workshop on Scene knowing for independent structures and the Workshop on powerful neighborhood Descriptors for computing device imaginative and prescient. LNCS 9009 includes the papers chosen for the Workshop on rising subject matters on snapshot recovery and Enhancement, the 1st overseas Workshop on strong examining, the second one Workshop on User-Centred desktop imaginative and prescient, the overseas Workshop on Video Segmentation in laptop imaginative and prescient, the Workshop: My automobile Has Eyes: clever automobile with imaginative and prescient know-how, the 3rd Workshop on E-Heritage and the Workshop on machine imaginative and prescient for Affective Computing. LNCS 9010 includes the papers chosen for the Workshop on characteristic and Similarity for laptop imaginative and prescient, the 3rd overseas Workshop on clever cellular and selfish imaginative and prescient and the Workshop on Human identity 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
0 dataset has high resolution so that faces are usually big and clear enough for OpenCV detector to detect. The precision was signiﬁcantly enhanced by introducing ﬂow score. The ﬁrst three rows of Fig. 4 show the eﬀectiveness of our detector over our baseline and ﬂow based detector. ’s detector sometimes failed to detect moving hands, mostly due to their unclear shape, our detector, by considering motion information, could detect them. This demonstrates that motion cue is extremely important for detecting hands in videos.
This work was supported in part by the JST CREST “Behavior Understanding based on Intention-Gait Model” project. 16 H. El-Alfy et al. References 1. : Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans. Circ. Syst. Video Technol. 22, 966–980 (2012) 2. : Gait recognition using a view transformation model in the frequency domain. , Pinz, A. ) ECCV 2006. LNCS, vol. 3953, pp. 151–163. Springer, Heidelberg (2006) 3. : View-invariant estimation of height and stride for gait recognition.
Number of subject in each subset of the OU-ISIR dataset. Dataset A-55 A-65 A-75 A-85 A-All No. of subjects 3706 3770 3751 3249 3141 As mentioned earlier in Sect. 3, we have developed a frame-based variant and a period-based variant. In addition, several parameters, such as the number of histogram bins and the number of grid boxes can be tuned to enhance the performance. In this section, we compare the variants of our approach and study the eﬀect of varying some parameters on the results. Finally, we compare it to other recent methods.