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ChinaVR 2021

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Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Passive haptic interaction method for multiple virtual targets in vast virtual reality space Jieke WANG, Lin LI, Hailong ZHANG, Liping ZHENG Journal of Computer Applications    2022, 42 (11): 3544-3550.   DOI: 10.11772/j.issn.1001-9081.2021122123 Abstract （321）   HTML （7）    PDF （2818KB）（90）       Knowledge map    Save Focused on the issue that real interaction targets cannot be matched with the virtual interaction targets one by one when providing passive haptics for redirected walking users in a vast Virtual Reality （VR） space， a method with two physical proxies acting as haptic proxies to provide haptic feedback for multiple virtual targets was proposed， in order to meet the user’s passive haptic needs alternately during the redirected walking process based on Artificial Potential Field （APF）. Aiming at the misalignment of virtual and real targets caused by the redirected walking algorithm itself and inaccurate calibration， the position and orientation of the virtual target were designed and haptic retargeting was introduced in the interaction stage. Simulation experimental results show that the design of the virtual target position and orientation can reduce the alignment error greatly. User experiments prove that haptic retargeting further improves the interaction accuracy and can bring users a richer and more immersive experience. Table and Figures | Reference | Related Articles | Metrics Select Cross‑resolution person re‑identification by generative adversarial network based on multi‑granularity features Yanbing GENG, Yongjian LIAN Journal of Computer Applications    2022, 42 (11): 3573-3579.   DOI: 10.11772/j.issn.1001-9081.2021122124 Abstract （321）   HTML （6）    PDF （2598KB）（116）       Knowledge map    Save Existing Super Resolution （SR） reconstruction methods based on Generative Adversarial Network （GAN） for cross?resolution person Re?IDentification （ReID） suffer from deficiencies in both texture structure content recovery and feature consistency maintenance of the reconstructed images. To solve these problems， a cross?resolution pedestrian re?identification method based on multi?granularity information generation network was proposed. Firstly， a self?attention mechanism was introduced into multiple layers of generator to focus on multi?granularity stable regions with structural correlation， focusing on recovering the texture and structure information of the Low Resolution （LR） person image. At the same time， an identifier was added at the end of the generator to minimize the loss in different granularity features between the generated image and the real image during the training process， improving the feature consistency between the generated image and the real image in terms of features. Secondly， the self?attention generator and identifier were jointed， then they were optimized alternately with the discriminator to improve the generated image on content and features. Finally， the improved GAN and person re?identification network were combined to train the model parameters of the optimized network alternately until the model converged. Comparison Experimental results on several cross?resolution person re?identification datasets show that the proposed algorithm improves rank?1 accuracy on Cumulative Match Characteristic（CMC） by 10 percentage points on average， and has better performance in enhancing both content consistency and feature expression consistency of SR images. Table and Figures | Reference | Related Articles | Metrics Select Review of eye movement‑based interaction techniques for virtual reality systems Shouming HOU, Chaolan JIA, Mingmin ZHANG Journal of Computer Applications    2022, 42 (11): 3534-3543.   DOI: 10.11772/j.issn.1001-9081.2021122134 Abstract （679）   HTML （33）    PDF （1617KB）（455）       Knowledge map    Save Eye movement?based human?computer interaction can enhance the immersion and improve comfort of users by using eye?movement characteristics， and the incorporating of eye movement?based interaction techniques in Virtual Reality （VR） system plays a vital role in the popularity of VR systems， which has become a research hotspot in recent years. Firstly， the principles and categories of VR eye movement?based interaction techniques were described， the advantages of combining VR systems with eye movement?based interaction techniques were analyzed， and the current mainstream VR head?mounted display devices and typical application scenarios were summarized. Then， based on the analysis of experiments related to VR eye tracking， the research hotspots of VR eye movement were summarized， including miniaturized equipment， diopter correction， lack of high?quality content， blurring and distortion of eyeball images， positioning accuracy and near?eye display system， and the corresponding solutions were prospected for those related hot issues. Table and Figures | Reference | Related Articles | Metrics Select Forest pest detection method based on attention model and lightweight YOLOv4 Haiyan SUN, Yunbo CHEN, Dingwei FENG, Tong WANG, Xingquan CAI Journal of Computer Applications    2022, 42 (11): 3580-3587.   DOI: 10.11772/j.issn.1001-9081.2021122164 Abstract （426）   HTML （8）    PDF （4972KB）（146）       Knowledge map    Save Aiming at the problems of slow detection speed， low precision， missed detection and false detection of current forest pest detection methods， a forest pest detection method based on attention model and lightweight YOLOv4 was proposed. Firstly， a dataset was constructed and preprocessed by using geometric transformation， random color dithering and mosaic data augmentation techniques. Secondly， the backbone network of YOLOv4 was replaced with a lightweight network MobileNetV3， and the Convolutional Block Attention Module （CBAM） was added to the improved Path Aggregation Network （PANet） to build the improved lightweight YOLOv4 network. Thirdly， Focal Loss was introduced to optimize the loss function of the YOLOv4 network model. Finally， the preprocessed dataset was input into the improved network model， and the detection results containing pest species and location information were output. Experimental results show that all the improvements of the network contribute to the performance improvement of the model； compared with the original YOLOv4 model， the proposed model has faster detection speed and higher detection mean Average Precision （mAP）， and effectively solves the problem of missed detection and false detection. The proposed new model is superior to the existing mainstream network models and can meet the precision and speed requirements of real?time detection of forest pests. Table and Figures | Reference | Related Articles | Metrics Select Visual‑saliency‑driven reuse algorithm of indirect lighting in 3D scene rendering Shujie QI, Chunyi CHEN, Xiaojuan HU, Haiyang YU Journal of Computer Applications    2022, 42 (11): 3551-3557.   DOI: 10.11772/j.issn.1001-9081.2021122181 Abstract （288）   HTML （2）    PDF （2946KB）（102）       Knowledge map    Save In order to accelerate rendering of 3D scenes by path tracing， a visual?saliency?driven reuse algorithm of indirect lighting in 3D scene rendering was proposed. Firstly， according to the characteristics of visual perception that the regions of interest have high saliency， while other regions have low saliency， a 2D saliency map of the scene image was obtained， which consists of color information， edge information， depth information and motion information of the image. Then， the indirect lighting in the high?saliency area was re?rendered， while the indirect lighting of the previous frame was reused in the low?saliency area under certain conditions， thereby accelerating the rendering. Experimental results show that the global lighting effect of the image generated by this method is real， and the rendering speed of the method is improved in several experimental scenes， and the speed can reach up to 5.89 times of that of the high?quality rendering. Table and Figures | Reference | Related Articles | Metrics Select Violence detection in video based on temporal attention mechanism and EfficientNet Xingquan CAI, Dingwei FENG, Tong WANG, Chen SUN, Haiyan SUN Journal of Computer Applications    2022, 42 (11): 3564-3572.   DOI: 10.11772/j.issn.1001-9081.2021122153 Abstract （470）   HTML （11）    PDF （2885KB）（134）       Knowledge map    Save Aiming at the problems of large model parameters， high computational complexity and low accuracy of traditional violence detection methods， a method of violence detection in video based on temporal attention mechanism and EfficientNet was proposed. Firstly， the foreground image obtained by preprocessing the dataset was input to the network model to extract the video features， meanwhile， the frame-level spatial features of violence were extracted by using the lightweight EfficientNet， and the global spatial-temporal features of the video sequence were further extracted by using the Convolutional Long Short-Term Memory （ConvLSTM） network. Then， combined with temporal attention mechanism， the video-level feature representations were obtained. Finally， the video-level feature representations were mapped to the classification space， and the Softmax classifier was used to classify the video violence and output the detection results， realizing the violence detection of video. Experimental results show that the proposed method can decrease the number of model parameters， reduce the computational complexity， increase the accuracy of violence detection and improve the comprehensive performance of the model with limited resources. Table and Figures | Reference | Related Articles | Metrics Select Object detection algorithm combined with optimized feature extraction structure Nan XIANG, Chuanzhong PAN, Gaoxiang YU Journal of Computer Applications    2022, 42 (11): 3558-3563.   DOI: 10.11772/j.issn.1001-9081.2021122122 Abstract （381）   HTML （3）    PDF （1607KB）（156）       Knowledge map    Save Concerning the problem of low object detection precision of DEtection TRansformer （DETR） for small targets， an object detection algorithm with optimized feature extraction structure， called CF?DETR （DETR combined CSP?Darknet53 and Feature pyramid network）， was proposed on the basis of DETR. Firstly， CSP?Darknet53 combined with the optimized Cross Stage Partial （CSP） network was used to extract the features of the original image， and feature maps of 4 scales were output. Secondly， the Feature Pyramid Network （FPN） was used to splice and fuse the 4 scale feature maps after down?sampling and up?sampling， and output a 52×52 size feature map. Finally， the obtained feature map and the location coding information were combined and input into the Transformer to obtain the feature sequence. Through the Forward Feedback Networks （FFNs） as the prediction head， the category and location information of the prediction object was output. On COCO2017 dataset， compared with DETR， CF?DETR has the number of model hyperparameters reduced by 2×106， the average detection precision of small objects improved by 2.1 percentage points， and the average detection precision of medium? and large?sized objects improved by 2.3 percentage points. Experimental results show that the optimized feature extraction structure can effectively improve the DETR detection precision while reducing the number of model hyperparameters. Table and Figures | Reference | Related Articles | Metrics