Nongliang Sun

In this paper, in response to the development of science and technology leading to the development of inkstone culture heritage is hindered, the design and realization of the inkstone culture virtual museum, the use of 3DsMax to build a Chinese cultural style museum scene, design based on the MVC architecture under the backpack system to achieve the museum collection of inkstone overall display, the design of the ScriptableObject storage list for the collection of inkstone specific information and inkstone model for Lightweight storage, according to the virtual reality collision detection technology, human-computer interaction technology, animation system design to realize the scene roaming in the museum, human-computer interaction, data management, cultural and creative game content, to increase the gamification of cultural and creative content, to achieve the inkstone culture digital protection and inheritance.

Due to the high risk factors in the electric power industry, the safety of power system can be improved by using the surveillance system to predict and warn the operators’ nonstandard and unsafe actions in real time. In this paper, aiming at the real-time and accuracy requirements in video intelligent surveillance, a method based on edge computing architecture is proposed to judge unsafe actions of electric power operations in time. In this method, the service of unsafe actions judgment is deployed to the edge cloud, which improves the real-time performance. In order to identify the action being executed, the end-to-end action recognition model proposed in this paper uses the Temporal Convolutional Neural Network (TCN) to extract local temporal features and a Gate Recurrent Unit (GRU) layer to extract global temporal features, which increases the accuracy of action fragment recognition. The result of action recognition is combined with the result of equipment target recognition based on the yolov3 model, and the classification rule is used to determine whether the current action is safe. Experiments show that the proposed method has better real-time performance, and the proposed action cognition is verified on the MSRAction Dataset, which improves the recognition accuracy of action segments. At the same time, the judgment results of unsafe actions also prove the effectiveness of the proposed method.

In order to obtain the discriminative compact appearance model for tracking objects effectively, this paper proposes a new structural tracking strategy that includes multicue inverse sparse appearance model and optimal metric evaluation between online robust templates and a limited number of particle samples in the looping process. Multicue inverse sparse appearance model globally improves the efficient selection of informative particle samples that can avoid the cumbersome coding and decoding cost for the trivial random particle samples. Only the most potential crucial cases are involved in each tracking loop. This refrains from unreasonable, rough numerical reduction of particle samples and also keeps the unbiasedness and dynamic stochasticness of the sampling process. Meanwhile, low-rank self-representatives for positive and negative samples facilitate the formulation of a suitable code book that arranges the useful sparse coefficients for feature bags and facilitates optimal metric evaluation for online training. It also alleviates the accuracy degradation of tracking occluded objects and improves the robustness of the tracker. Both of them preserve the discriminative compactness of target which speeds up particle filtering localization to separate the target object from distractors. Moreover, the proposed method exploits online appearance representations to learn the sharing compact information that avoids massive calculation burdens for massive visual data.

In order to reduce the calculation cost and improve the robustness of appearance model, this paper presents an optimal object tracking method that consists of improved inverse sparse representation and global spatial envelope. First, partial least squares regression-based structural model is adopted, which easily facilitates target template sparsely represented by candidate dictionary. Furthermore, candidates with nonzero coefficients are easily selected as possible tracking results. Meanwhile, partial occlusion and slight appearance changes are effectively alleviated during the tracking process. Second, spatial envelope in the frequency domain is utilized to select the best candidate from the inverse sparse representation process. Multiple scales and orientations-based Gabor filters are established to obtain the Gist information, which keeps the potential structural attributes of local appearance models to tolerate appearance variation. In addition, the Bayesian inference framework is used to exploit candidate samples, and a simple model update scheme is employed to alleviate drifting caused by temporal varying multi-factors. The qualitative experimental results show that the proposed tracking algorithm provides a better performance in some dynamic scenes.

The eyes are the most important facial landmarks on human face. It is significant to extract the feature of eyes efficiently. In order to improve the real-time and veracity performance of the eye location method, this paper presents a coarse-to-fine eye location method based on ring Gabor filter (RGF). First an elliptical Gabor filter is devised to convolute with the face image to highlight the eye region, which can be exploited to segment the two pupil regions efficiently. After getting the region of the pupil, a circular Gabor filter is used to locate the centre of the eyeball. Extensive experiments on the face base show that the method can locate the pupils accurately. Comparing with other algorithms, results prove that this method is robust to the variations of face poses, expressions, accessories and illuminations.