Video Time: Properties, Encoders and Evaluation

Time-aware encoding of frame sequences in a video is a fundamental problem in video understanding. While many attempted to model time in videos, an explicit study on quantifying video time is missing. To fill this lacuna, we aim to evaluate video time explicitly. We describe three properties of video time, namely a) temporal asymmetry, b)temporal continuity and c) temporal causality. Based on each we formulate a task able to quantify the associated property. This allows assessing the effectiveness of modern video encoders, like C3D and LSTM, in their ability to model time. Our analysis provides insights about existing encoders while also leading us to propose a new video time encoder, which is better suited for the video time recognition tasks than C3D and LSTM. We believe the proposed meta-analysis can provide a reasonable baseline to assess video time encoders on equal grounds on a set of temporal-aware tasks.Comment: 14 pages, BMVC 201

DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informative-ness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because i) it re-uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that our DeepProposals outperform most of the previously proposed object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.Comment: 15 page

Online Action Detection

In online action detection, the goal is to detect the start of an action in a video stream as soon as it happens. For instance, if a child is chasing a ball, an autonomous car should recognize what is going on and respond immediately. This is a very challenging problem for four reasons. First, only partial actions are observed. Second, there is a large variability in negative data. Third, the start of the action is unknown, so it is unclear over what time window the information should be integrated. Finally, in real world data, large within-class variability exists. This problem has been addressed before, but only to some extent. Our contributions to online action detection are threefold. First, we introduce a realistic dataset composed of 27 episodes from 6 popular TV series. The dataset spans over 16 hours of footage annotated with 30 action classes, totaling 6,231 action instances. Second, we analyze and compare various baseline methods, showing this is a challenging problem for which none of the methods provides a good solution. Third, we analyze the change in performance when there is a variation in viewpoint, occlusion, truncation, etc. We introduce an evaluation protocol for fair comparison. The dataset, the baselines and the models will all be made publicly available to encourage (much needed) further research on online action detection on realistic data.Comment: Project page: http://homes.esat.kuleuven.be/~rdegeest/OnlineActionDetection.htm

Estimating greenhouse gas emissions using emission factors from the Sugarcane Development Company, Ahvaz, Iran

Background: Greenhouse gas (GHG) emissions are increasing worldwide. They have harmful effects on human health, animals, and plants and play a major role in global warming and acid rain. Methods: This research investigated carbon dioxide (CO2) and CH4 emissions obtained from different parts of the Hakim Farabi, Dobal Khazaei, and Ramin factories which produce ethanol and yeast. Seasonal rates of CO2 at the soil surface at the studied sites were estimated from measurements made on location and at intervals with manual chambers. This study aimed to assess the production rate of GHG emissions (CH4, CO2) in the sugar production units of Hakim Farabi, Dobal Khazaei, and Ramin factories. Results: Mean concentrations of CO2 and CH4 emissions are respectively 279 500.207 and 3087.07 tons/ year from the Hakim Farabi agro-industry, 106 985.24 and 1.14 tons/year at the Dobal Khazaei ethanol producing factory, and 124 766.17 and 1.93 tons/year at the Ramin leavening producing factory. Conclusion: Sugar plant boilers and the burning of sugarcane contributed the most CO2 and CH4 emissions, respectively. Moreover, lime kilns and diesel generators showed the least carbon dioxide and methane emissions, respectively. Keywords: Carbon Dioxide, Methane, Ethanol, Farms, Global Warmin

Actor and Action Video Segmentation from a Sentence

This paper strives for pixel-level segmentation of actors and their actions in video content. Different from existing works, which all learn to segment from a fixed vocabulary of actor and action pairs, we infer the segmentation from a natural language input sentence. This allows to distinguish between fine-grained actors in the same super-category, identify actor and action instances, and segment pairs that are outside of the actor and action vocabulary. We propose a fully-convolutional model for pixel-level actor and action segmentation using an encoder-decoder architecture optimized for video. To show the potential of actor and action video segmentation from a sentence, we extend two popular actor and action datasets with more than 7,500 natural language descriptions. Experiments demonstrate the quality of the sentence-guided segmentations, the generalization ability of our model, and its advantage for traditional actor and action segmentation compared to the state-of-the-art.Comment: Accepted to CVPR 2018 as ora

Signal processing based damage detection of concrete bridge piers subjected to consequent excitations

Damage detection at an early stage is of great importance especially for infrastructures since the cost of repair is considerably less than that of reconstruction. The change in stiffness and frequency could obviously indicate the occurrence of damage and its severity. Wavelet transform is a powerful mathematical tool for signal processing which provides more details compared to Fourier transform. In this paper, a model-free output-only wavelet-based damage detection analysis has been performed in order to achieve perturbation of detailed function of acceleration response in bridge piers. First, a nonlinear time-history finite element analysis was performed using 9 consequent earthquake records; from which, time-history acceleration response was derived. Also pushover and hysteresis curves were drawn based on the results. Furthermore, applying wavelet transform to structural response, some irregularities appeared in decomposed detailed function which imply on damage presence in the models. Finally, peak values of details could lead us to time instants of damage