No abstract

No abstract

No abstract

No abstract

No abstract

The computer vision community has taken a keen interest in recent developments in activity recognition and classification in sports videos. Advancements in sports have a broadened the technical interest of the computer vision community to perform various types of research. Images and videos are the most frequently used components in computer vision. There are numerous models and methods that can be used to classify videos. At the same time, there no specific framework or model for classifying and identifying sports videos. Hence, we proposed a framework based on deep learning to classify sports videos with their appropriate class label. The framework is to perform sports video classification using two different benchmark datasets, UCF101 and the Sports1-M dataset. The objective of the framework is to help sports players and trainers to identify specific sports from the large data source, then analyze and perform well in the future. This framework takes sports video as an input and produces the class label as an output. In between, the framework has numerous intermediary processes. Preprocessing is the first step in the proposed framework, which includes frame extraction and noise reduction. Keyframe selection is carried out by candidate frame extraction and an enhanced threshold-based frame difference algorithm, which is the second step. The final step of the sports video classification framework is feature extraction and classification using CNN. The proposed framework result is compared with pretrained neural networks such as AlexNet and GoogleNet, and then the results are also compared. Three different evaluation metrics are used to measure the accuracy and performance of the framework.

No abstract

Data embedding techniques embed the secret image into another image for increasing the privacy. The data embedding techniques can also be substituted to the videos so that the confidentiality of the image, video, and the embedded data can be maintained. In this paper, multiple compression techniques such as Principle Component Analysis (PCA) based method, Set Partitioning in Hierarchical Trees (SPIHT) algorithm and fuzzy concepts are analyzed. The embedding techniques are classified into two types such as digital image watermarking and data hiding algorithms. The digital watermarking techniques like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Least Significant Bit (LSB) are surveyed. Further, the data hiding techniques such as H.264/AVC video stream and MPEG videos are analyzed. In the survey results, it is clear that the existing techniques do not efficiently restore the compressed image, the pixel information is lost during the transformations. Further, the existing techniques have increased time complexity and computational complexity.