Computer Vision – ECCV 2016: 14th European Conference, by Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

By Bastian Leibe, Jiri Matas, Nicu Sebe, Max Welling

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed complaints of the 14th eu convention on laptop imaginative and prescient, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016.
The 415 revised papers awarded have been conscientiously reviewed and chosen from 1480 submissions. The papers conceal all features of computing device imaginative and prescient and trend acceptance comparable to 3D laptop imaginative and prescient; computational images, sensing and show; face and gesture; low-level imaginative and prescient and picture processing; movement and monitoring; optimization equipment; physics-based imaginative and prescient, photometry and shape-from-X; popularity: detection, categorization, indexing, matching; segmentation, grouping and form illustration; statistical tools and studying; video: occasions, actions and surveillance; purposes. they're geared up in topical sections on detection, reputation and retrieval; scene realizing; optimization; photograph and video processing; studying; motion, task and monitoring; 3D; and nine poster sessions.

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Extra resources for Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII

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Riots), where individuals do not have enough time and space to control their movements. Hence, they are subject to unintentional physical body contacts that may strongly affect their movements. Borrowing from [7,25], the body contact force imposed on i from j is formulated as: Fijbc = nji · gi (j) (2) where gi (j) is a function that returns zero if i and j are not close enough to have body contact and a scalar value inversely proportional to their spatial distance dij , otherwise. Heuristic rule H3: In violent situations, individual j may exhibit an action (verbally, emotionally or physically) to individual i that triggers i to move towards j for a reaction [26].

While previous studies [21,36,37] have attempted to apply the regression estimation method for reflection estimation, most of them were limited to theoretical studies on small datasets of known “generic” spectra (such as the Munsell color chip set) or to domain specific tasks [36]. Despite their limited scope, these studies indicate that accurate spectral recovery may be achieved from RGB data. Further optimism may be garnered from the recent work of Xing et al. [38] demonstrating noise reduction and data recovery in hyperspectral images based on a sparse spatio-spectral dictionary.

We first construct the feature vector by getting the average deep features vector of 10 jittered samples of the original image. Then, we L2 normalized the feature vectors, and evaluate its performance on VIC, VIM, and BEHAVE datasets. We performed violence classification at video level for VIC, VIM, and VC datasets. For the first two datasets, we followed the standard training-testing splits that come with each dataset, whilst for the VC we equally divide each class into a test set of 150 videos (50 video sequences for each class) and the rest for testing.

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