Broadcasters produce enormous numbers of sport videos in cyberspace due to massive viewership and commercial benefits. Manual processing of such content for selecting the important game segments is a laborious activity; therefore, automatic video content analysis techniques are required to effectively handle the huge sports video repositories. The sports video content analysis techniques consider the shot classification as a fundamental step to enhance the probability of achieving better accuracy for various important tasks, i.e., video summarization, key-events selection, and to suppress the misclassification rates. Therefore, in this research work, we propose an effective shot classification method based on AlexNet Convolutional Neural Networks (AlexNet CNN) for field sports videos. The proposed method has an eight-layered network that consists of five convolutional layers and three fully connected layers to classify the shots into long, medium, close-up, and out-of-the-field shots. Through the response normalization and the dropout layers on the feature maps we boosted the overall training and validation performance evaluated over a diverse dataset of cricket and soccer videos. In comparison to Support Vector Machine (SVM), Extreme Learning Machine (ELM), K-Nearest Neighbors (KNN), and standard Convolution Neural Network (CNN), our model achieves the maximum accuracy of 94.07%. Performance comparison against baseline state-of-the-art shot classification approaches are also conducted to prove the superiority of the proposed approach.

Aging affects left and right half face differently owing to numerous factors such as sleeping habits, exposure to sun light, and weaker face muscles of one side of face. In computer vision, age of a given face image is estimated using features that are correlated with age, such as moles, scars, and wrinkles. In this study we report the asymmetric aging of the left and right sides of face images and its impact on accurate age estimation. Left symmetric faces were perceived as younger while right symmetric faces were perceived as older when presented to the state‐of‐the‐art age estimator. These findings show that facial aging is an asymmetric process which plays role in accurate facial age estimation. Experimental results on two large datasets verify the significance of using asymmetric right face image to estimate the age of a query face image more accurately compared to the corresponding original or left asymmetric face image.

Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.

One of the most dominant factors in developing tactile modules is the ability to generate abundant vibrotactile sensation. This paper presents a new vibrotactile module which can stimulate two mechanoreceptors at the same time without any mechanical vibration motors. To realize that, we first design an electro-tactile beat module (an ETB module) consisting of a lower part, a connection part and an upper part. The two electrodes were designed in an interdigitated pattern and were applied to the upper part. By applying two voltage inputs with slightly different frequencies to two electrodes in the proposed ETB module, respectively, we can create beat-patterned vibration. Furthermore, we can create normal vibration with the proposed ETB module by applying same frequency to the two electrodes. Experiments were conducted to validate the haptic performance of the proposed prototype. The results show that the proposed ETB module can create not only beat-patterned vibration but also normal vibration. The results also show that it can generate strong enough vibration to stimulate mechanoreceptors in wide frequency ranges.