In-situ detection and recognition of hand gestures in an unconstrained environment is a crucial task in the field of machine vision and video recognition. Hand gesture recognizing task is perhaps more complicated, with varying lighting conditions and background. Robust, accurate hand detection and classification becomes necessary to facilitate the Graphical User Interface, Tele-surgery, etc. In this paper, a Convolutional Neural Network (CNN) based Hybrid Single Stage Recognition (Hybrid-SSR) framework is proposed for real-time hand gesture recognition. The time-efficient Enhanced Xception (E-Xception) CNN model is proposed for feature extraction. The framework is evaluated using three separate datasets (NUSHP-II, Senz-3D, and MITI-HD). The performance of the framework is evaluated for the range of IoU from 0.5 to 0.95. In comparison with the conventional single-stage hand gesture recognition system, the Hybrid-SSR model resulted in higher precision values (99.60 % on AP0.5, 97.80 % on AP0.75, and 88.20 % on AP0.5:0.95) for the MITI-HD dataset. The Hybrid-SSR model with Adam Optimizer surpasses the other optimization techniques. The proposed model achieved comparatively higher precision and recall values for focusing parameter (γ) = 2 and weighting parameter (α) = 0.5. The misclassification problem encountered by the conventional single-stage models is mitigated by implementing the focal loss function in the classification sub-network. The computation cost of Hybrid-SSR (12 ms) is significantly lower compared to other single-stage hand gesture recognition frameworks. 

In-situ recognition of hand gesture via Enhanced Xception based single-stage deep convolutional neural network

The invention provides a three-parameter feature fusion gesture recognition method based on a FMCW (frequency modulated continuous wave) radar. Firstly, the IF signals of different gestures are acquired by radar, from which the angle parameters, range parameters and Doppler frequency shift parameters are obtained, and the corresponding data sets are constructed. Secondly, the data sets of angle, range and Doppler frequency shift are trained in convolutional neural network, and the eigenvalues are obtained and fused. Finally, the fused eigenvalues are sent into the classifier to get the gestureclass. The invention innovatively proposes a fusion method of multiple parameters in a convolution neural network, solves the problem that the use condition of the traditional recognition algorithm is limited, and the classification effect of the final gesture is better than that of a single parameter.

A three-parameter feature fusion gesture recognition method based on FMCW radar

In-situ identification and recognition of multi-hand gestures using optimized deep residual network

The invention discloses a three-dimensional gesture key point detection method based on a neural network, which comprises the following steps: acquiring a gesture data set including gesture area information and gesture two-dimensional and three-dimensional key point position information; obtaining a gesture data set containing gesture area information and gesture two-dimensional and three-dimensional key point position information. Training a gesture segmentation network which can detect a gesture region in an RGB image by taking an RGB image containing a gesture as an input; the gesture region detected by the gesture segmentation network is truncated, up-sampled or down-sampled. Training a two-dimensional gesture key point detection network, which can detect a plurality of two-dimensionalgesture key points in a gesture region image; the absolute coordinates of the key points of the three-dimensional gesture are converted into relative coordinates; a 2D to 3D gesture key point mappingnetwork is trained. The network can map multiple 2D gesture key points into 3D space to form 3D gesture key points. The invention can quickly and effectively detect three-dimensional gesture key points from RGB images containing gestures.

A method and a system for detecting key points of a three-dimensional gesture based on a neural network

The embodiment of the invention is applicable to the technical field of pattern recognition, and discloses a human body action recognition method and device, terminal equipment and a computer readablestorage medium, and the method comprises the steps: obtaining a depth image sequence of a human body action; Dividing the depth image sequence into a preset number of image sequence segments at equalintervals; Performing time sequence sparse sampling on each image sequence segment to obtain a corresponding target image sequence; Extracting a gradient direction vector of each target image sequence; And according to the gradient direction vector and a pre-trained three-dimensional convolutional neural network model, human body action recognition is carried out According to the embodiment of the invention, the space-time information description capability is relatively high, the recognition performance is relatively high, and the calculation is relatively simple

Human body action recognition method and device, terminal equipment and storage medium

The invention belongs to gesture recognition methods and relates to a 3d convolutional neural network based gesture recognition method. A series of preprocessing is carried out aiming at common videosand depth videos shot by a depth camera. On the basis of application of a common processing method for preprocessing, a convolutional neural sub-network and a deconvolution neural sub-network combined denoising method is adopted aiming at the problem of noise points of video images, and a 3d convolutional neural network is adopted for processing aimed at a temporal-spatial relationship in the videos. By the 3d convolutional neural network based gesture recognition method, the gesture classification rate is substantially increased, and recognition basis reliability and result reasonability areimproved.

3d convolutional neural network based gesture recognition method

Hand gesture recognition on the depth videos is a promising approach for automotive interfaces because it is less sensitive to light variation and more accurate than other traditional methods. However, video gestures recognition is still a challenging task since lots of interferences are induced by the uncorrelated gesture factors. Considering that if the displays are more relevant, the results will more accurate, so ResNext, a kind of compact and efficient neural network, is firstly used as feature extractor, then an improved weighted frame unification method is adopted to obtain the key frame samples, finally the Discriminant correlation analysis (DCA) is employed to fuse features for static data and dynamic data after conducting Feature embedding branch (FEB) on static data. The public dataset named Depth based gesture recognition database (DGRD) is used in this paper, but the dataset is a little small and the class distribution is largely imbalance, and we find the performance of ResNext degrades badly in the condition of imbalance problem although it achieves excellent result at sufficient training data. In order to conquer the disadvantages of limited dataset, a special loss function scheme combining the softmax loss and dice loss is proposed. Evaluation of the algorithm performances in comparison with other state-of-the-art methods indicates that the proposed method is more practical for gesture recognition and may be widely adopted by automotive interfaces.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cje.2020.06.008

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces

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3d convolutional neural network based gesture recognition method

Feature Fusion Based Hand Gesture Recognition Method for Automotive Interfaces