No abstract

The objective of this work is to propose a novel idea of transforming temporal redundancies present in videos. Initially, the frames are divided into sub-blocks. Then, the temporally redundant blocks are grouped together thus generating new frames with spatially redundant temporal data. The transformed frames are given to compression in the wavelet domain. This new approach greatly reduces the computational time. The reason is that the existing video codecs use block matching methods for motion estimation which is a time consuming process. The proposed method avoids the use of block matching method. The existing H.264/AVC takes approximately one hour to compress a video file where as the proposed method takes only one minute for the same task. The experimental results substantially proved that the proposed method performs better than the existing H.264/AVC standard in terms of time, compression ratio and PSNR.

Fall detection is a serious problem in elder people. Constant inspection is important for this fall identification. Currently, numerous methods associated with fall detection are a significant area of research for safety purposes and for the healthcare industries. The objective of this paper is to identify elderly falls. The proposed method introduces keyframe based fall detection in elderly care system. Experiments were conducted on University of Rzeszow (UR) Fall Detection dataset, Fall Detection Dataset and MultiCam dataset. It is substantially proved that the proposed method achieves higher accuracy rate of 99%, 98.15% and 99% for UR Fall detection dataset, Fall Detection Dataset and MultiCam dataset, respectively. The performance of the proposed method is compared with other methods and proved to have higher accuracy rate than those methods.

The objective of this paper is to develop a shot boundary detection system to detect shots from a given video. To meet this objective, it is proposed to use feature vector in the process of identifying cut transition and gradual transition. The proposed method analyzes each video frame with its immediate left and right neighbor frame and identifies shot transitions. For each frame comparison, it considers color, edge, motion, and texture features of left and right frames. The proposed method is evaluated on video sequences from the TRECVID video set. The experimental results of the proposed method are compared with the existing methods. It is observed that the proposed method is suitable to perform robust shot boundary detection.

In video sequence coding, a combination of temporal and spatial coding technique is used in order to remove the predictable or redundant image content and encode only the unpredictable information. The objective of video compression technique is to increase the coding efficiency and to increase the data rate savings. A segmentation-based compression method is proposed to achieve this goal. In this paper, static portions are identified using frame differencing method and segmented during the encoding process. This encoded information is passed to the synthesiser. In synthesis, information which is passed during segmentation process is added to reconstruct the video. This method is integrated with the conventional video codec H.264/AVC video codec. Experimental results substantially proved the data rate is reduced by as much as 25%.