Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature Extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature Extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority.

Statistical survey and comprehensive review on human skin detection

With the advancement of data society today, pictures have turned out to be increasingly imperative. Automatic detection of human skin has been an area of active research for the past few years. Human skin detection assumes a vital job in a wide scope of picture preparing applications going from face detection, steganography, face tracking, age detection, discover pornographic images, the discovery of skin diseases gesture analysis and substance based picture recovery frameworks and to different human PC association spaces. Detecting human skin in complex pictures have ended up being a difficult issue since skin shading can fluctuate drastically in its appearance because of numerous variables, for example, illumination, race, maturing, imaging conditions, and complex foundation. In this study, we will study and analyze skin researches, where we will treat the weakness of previous research of survey on human skin detection methods. The reason for this investigation is to give a state-of-the-art study on human skin molding and detection methods in 1998-2019 periods. Furthermore, this research presented the statistical study for each issue stated before. We finish up with a few ramifications for a future course. Study results will benefit all researchers who are interested in human skin detection topic.

GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector

Students’ gestures, hand-raising, stand-up, and sleeping, indicates the engagement of students in classrooms and partially reflects teaching quality. Therefore, fast and automatically recognizing these gestures are of great importance. Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs. Firstly, we build a large-scale corpus from real schools to capture various behavior gestures. Based on such a corpus, we transfer the gesture recognition task into object detections. Secondly, we design a multi-dimensional attention-based detector, named GestureDet, for real-time and accurate gesture analysis. The multi-dimensional attention mechanisms simultaneously consider all the dimensions of the training set, aiming to pay more attention to discriminative features and samples that are important for the final performance. Specifically, the spatial attention is constructed with stacked dilated convolution layers to generate a soft and learnable mask for re-weighting foreground and background features; the channel attention introduces the context modeling and squeeze-and-excitation module to focus on discriminative features; the batch attention discriminates important samples with a new designed reweight strategy. Experimental results demonstrate the effectiveness and versatility of GestureDet, which achieves 75.2% mAP on real student behavior dataset, and 74.5% on public PASCAL VOC dataset at 20fps on embedding device Nvidia Jetson TX2. Code will be made publicly available.