Transitional actions belong to a class between actions for short-term action prediction (see Figure 1). Early action recognition is necessary for producing action predictions in the early frames of an objective action. Earlier prediction in the initial frames of an objective action is desirable for early action recognition problems, but the solutions depend on the action itself. On one hand, within the setting of a shortterm action prediction, understanding a pending human action change is more natural if we have a firm grasp on transitional actions. In a traffic scene, short-term action predictions are particularly crucial for avoiding accidents between humans and vehicles. Figure 1 shows sequential actions that include Walk straight, Walk straight -cross, and cross. Where Walk straight and cross are conventional action definitions, our proposal adds a transitional action between actions (here Walk straight -cross) in order to provide a better action approach to predictions. Our proposed short-term predictions achieve earlier prediction than so-called early activity recognition, since they can recognize a dangerous cross action while it is transitional. Intuitively, the recognition difficulty arising from action and transitional action is that they tend to partially overlap each other. We believe that the use of a subtle motion descriptor (SMD) will allow us to identify sensitive differences between actions and transitional actions.In this paper, we address the recognition of transitional action for short-term action prediction. We also propose a discriminative temporal convolutional neural network (CNN) feature that can be used for recognizing transitional actions in order to overcome the difficulty of indistinguishable feature classification in transitional actions. To accomplish this, we employ an SMD that captures subtle differences between consecutive frames. Our paper contains two main contributions: (i) the definition of transitional action for short-term action prediction that achieves earlier prediction than early action recognition, and (ii) identifying CNN-based SMD to create a clear distinctions between action and transitional action. The feature is simply updated from a spatio-temporal CNN feature Pooled Time Series (PoT) proposed in [1].Our CNN-based SMD demonstrated the best rate of success on three different trial datasets. Even when using the shortest (3-frame) feature accumulation for recognition tuning, we confirmed outstanding results with 85.78% (NTSEL), 69.77% (UTKinect), and 49.93% (Watch-n-Patch) on the three different datasets.[1] M. S. Ryoo, B. Rothrock, and L. Matthies. Pooled motion features for first-person videos. CVPR, 2015.
Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understand how different the two images. Therefore, a semantic understanding is required in the change detection research such as disaster investigation. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multiscale feature representation captured by different image patches. We applied our method to the TSUNAMI Panoramic Change Detection dataset, and re-annotated the changed areas of the dataset via semantic classes. The results show that our multiscale hypermaps provided outstanding performance on the reannotated TSUNAMI dataset.
Visual Inspection is one of the important quality control unctions of production lines. In this area, many automatic visual inspection machines have been introduced and proved their effectiveness. Today, visual inspection is going to be applied to the human impression evaluation, such as the beauty of products, small and vague defects on pro ducts, etc. In these cases, it is very important to evaluate the human sensitivity of defects. Human sensitivity is called "KANSEI" in Japanese. In ths paper, fist, some of the examples of human sensitivity, "KANSEI", evaluation are described. Then, an attempt to evaluate the color sensitivity of human and an application of he evaluation result to visual inspection systems is introduced.-25-
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.
To propose evaluation methodology of proficiency of human actions, we have analyzed soccer inside-kick as the basic stage of research. We analyzed the differences between beginners and experts with appearance-based approach using Dense Trajectories (DT) and clarified valid features in DT to discriminate beginners from experts appropriately. After extraction of features using DT, Principal Component Analysis (PCA) was performed. The first and second-order principal components were adopted to draw a two-dimensional scatter diagram for visualization. HOF and MBHy were selected as effective features compared with HOG and MBHx.
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